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https://www.databricks.com/dataaisummit/speaker/michael-powell/#
Michael Powell - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingMichael PowellChief, Registry & Assessment Section Immunization Branch Division of Communicable Disease Control Center for Infectious Diseases at California Department of Public Health (CDPH)Back to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/jp/solutions/industries/retail-industry-solutions
小売・消費財業界向けデータレイクハウスソリューション | DatabricksSkip to main contentプラットフォームデータブリックスのレイクハウスプラットフォームDelta Lakeデータガバナンスデータエンジニアリングデータストリーミングデータウェアハウスデータ共有機械学習データサイエンス料金Marketplaceオープンソーステクノロジーセキュリティ&トラストセンターウェビナー 5 月 18 日午前 8 時 PT さようなら、データウェアハウス。こんにちは、レイクハウス。 データレイクハウスが最新のデータスタックにどのように適合するかを理解するために出席してください。 今すぐ登録ソリューション業種別のソリューション金融サービス医療・ライフサイエンス製造通信、メディア・エンターテイメント公共機関小売・消費財全ての業界を見るユースケース別ソリューションソリューションアクセラレータプロフェッショナルサービスデジタルネイティブビジネスデータプラットフォームの移行5月9日 |午前8時(太平洋標準時)   製造業のためのレイクハウスを発見する コーニングが、手作業による検査を最小限に抑え、輸送コストを削減し、顧客満足度を高める重要な意思決定をどのように行っているかをご覧ください。今すぐ登録学習ドキュメントトレーニング・認定デモ関連リソースオンラインコミュニティ大学との連携イベントDATA+AI サミットブログラボBeacons2023年6月26日~29日 直接参加するか、基調講演のライブストリームに参加してくださいご登録導入事例パートナークラウドパートナーAWSAzureGoogle CloudPartner Connect技術・データパートナー技術パートナープログラムデータパートナープログラムBuilt on Databricks Partner ProgramSI コンサルティングパートナーC&SI パートナーパートナーソリューションDatabricks 認定のパートナーソリューションをご利用いただけます。詳しく見る会社情報採用情報経営陣取締役会Databricks ブログニュースルームDatabricks Ventures受賞歴と業界評価ご相談・お問い合わせDatabricks は、ガートナーのマジック・クアドラントで 2 年連続でリーダーに位置付けられています。レポートをダウンロードDatabricks 無料トライアルデモを見るご相談・お問い合わせログインJUNE 26-29REGISTER NOW「リテール向けレイクハウス」の活用カスタマージャーニーのあらゆる段階における成果を最大化する小売業向けデータ、分析、AI の統合プラットフォームご登録ご相談・お問い合わせ高性能、スケーラビリティ、低コストリテール向けレイクハウスシェアリングとガバナンス機能を組み込んでデータと AI のワークロードを統合し、社内外のチームが必要なときに必要なデータにアクセスできるようにします。バリューチェーン全体への影響顧客エンゲージメントあらゆるタッチポイントに関連性とハイパーパーソナライゼーションをもたらすリアルタイムで正確な 360° の顧客ビューにより、小売業者は、チャネル全体のセンチメントを把握し、推薦をパーソナライズして、顧客のニーズに応じた関係を築するために必要な全てを入手できます。その結果、収益性とロイヤルティが向上します。運用効率の改善従業員の生産性向上製品性能の向上リテール向けソリューションとパートナー業界に特化した効果的なデータ分析・AI ソリューションDatabricks ソリューションアクセラレータは、成果創出を加速するフル機能の Notebook やベストプラクティスを含む目的に沿ったガイドです。ソリューションアクセラレータを使用することで、傾向スコアリング、顧客生涯価値、オーダーピッキングの最適化などのユースケースにおける発見、設計、開発、テストにかかる時間を短縮し、小売業の成果を加速させます。細粒度な需要予測短時間で大規模な予測を作成 Datbricks レイクハウスプラットフォームの分散型計算能力を活用し、店単位で細粒度な予測を効率的に行うことができます。無料トライアル顧客セグメンテーション顧客のセグメント化によるターゲティングの最適化 高度な顧客セグメントを作成し、販売データ、キャンペーン、プロモーションシステムなどを利用して行動に基づく購買予測を行います。無料トライアルリアルタイム POS 分析複数店舗の在庫をリアルタイムに計算し、小売店のマージンを改善 あらゆる種類の POS データを迅速に大規模に取り込み、リアルタイムのインサイトを導き出すことで、店舗における緊急の情報ニーズに対応します。無料トライアル小売業向けアクセラレーターを見るDatabricks は、主要なコンサルティングパートナーとの連携を通じて、各業界固有の革新的なソリューションを構築しています。深い専門知識と長年の経験を持つパートナーによる設計に基づく Databricks の Brickbuilder は、Databricks のレイクハウス向けに構築されており、コスト削減とデータ価値の最大化を可能にするソリューションです。お客様のニーズに最適なソリューションが見つかります。需要予測の統合ビュー単一ソースの需要計画で、正確性、粒度、適時性を最大化します。詳しく見るRGM(収益成長管理)請求書データ、外部市場データ、ニュース、Webスクレイピングデータを高速に分析し、小売パターンを探索することができます。詳しく見るTrellis ソリューション需要予測、補充、調達、価格、プロモーションサービスに関する課題を解決する。詳しく見るBrickbuilder ソリューションを見る「以前は非常に困難だった、オンラインとオフラインの購買情報をお客さまレベルで組み合わせることができるようになりました。このオムニチャネルビューにより、より包括的な推薦エンジンをオンラインで構築することができ、非常に多くのエンゲージメントを獲得しています。」 Jumbo 社 データサイエンス・分析マネージャー Wendell Kuling 氏 「Databricks により、あらゆる市場で最高のパフォーマンスを発揮し、クラス最高の顧客体験を提供できるようになりました。ステークホルダーと開発者がレイクハウスプラットフォームを活用して、1 億 8,000 万人のお客さまにサービスを提供するために必要な傾向と知見を引き出しています。」 Reckitt 社 データサイエンス部門責任者 Sergiy Tkachuk 氏 「データブリックスのプラットフォームを複数の事業部がセルフサービスで利用しています。これは以前には考えられないことでした。データブリックスの導入効果は非常に大きいと感じています。」 コロンビアスポーツウェア社 シニアエンタープライズデータマネージャー ララ・マイナー氏 関連リソースセルフガイドツアーリテール向けレイクハウスの探索に必要な全てのリソースWeb セミナー小売業におけるリアルタイムな意思決定の促進eBook小売業におけるユースケースのビッグブック製品プラットフォーム料金オープンソーステクノロジーDatabricks 無料トライアルデモ製品プラットフォーム料金オープンソーステクノロジーDatabricks 無料トライアルデモ学習・サポートドキュメント用語集トレーニング・認定ヘルプセンター法務オンラインコミュニティ学習・サポートドキュメント用語集トレーニング・認定ヘルプセンター法務オンラインコミュニティソリューション業種別プロフェッショナルサービスソリューション業種別プロフェッショナルサービス会社情報会社概要採用情報ダイバーシティ&インクルージョンDatabricks ブログご相談・お問い合わせ会社情報会社概要採用情報ダイバーシティ&インクルージョンDatabricks ブログご相談・お問い合わせ採用情報言語地域English (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.プライバシー通知|利用規約|プライバシー設定|カリフォルニア州のプライバシー権利
https://www.databricks.com/dataaisummit/speaker/sachin-balgonda-patil
Sachin Balgonda Patil - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingSachin Balgonda PatilSolutions Architect at DatabricksBack to speakersSachin is a Solutions Architect at Databricks and is based out of London, UK. He has spent around 20 years Architecting, Designing and Implementing complex production grade applications for various customers across the globe. In his prior role, he has implemented streaming applications for the financial services and has deep interest in real time streaming workloads. Before joining Databricks, he worked for a global system integration company.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/glossary/what-are-transformations
What are Transformations?PlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWTransformationsAll>TransformationsTry Databricks for freeGet StartedWhat Are Transformations?In Spark, the core data structures are immutable meaning they cannot be changed once created. This might seem like a strange concept at first, if you cannot change it, how are you supposed to use it? In order to “change” a DataFrame you will have to instruct Spark how you would like to modify the DataFrame you have into the one that you want. These instructions are called transformations. Transformations are the core of how you will be expressing your business logic using Spark. There are two types of transformations, those that specify narrow dependencies and those that specify wide dependencies.What Are Narrow Dependencies?Transformations consisting of narrow dependencies [we’ll call them narrow transformations] are those where each input partition will contribute to only one output partition. What Are Wide Dependencies?A wide dependency [or wide transformation] style transformation will have input partitions contributing to many output partitions. You will often hear this referred to as a shuffle where Spark will exchange partitions across the cluster. With narrow transformations, Spark will automatically perform an operation called pipelining on narrow dependencies, this means that if we specify multiple filters on DataFrames they’ll all be performed in-memory. The same cannot be said for shuffles. When we perform a shuffle, Spark will write the results to disk. You’ll see lots of talks about shuffle optimization across the web because it’s an important topic but for now all you need to understand are that there are two kinds of transformations. Additional ResourcesOptimized Data Transformation DocumentationWhich Data Broke My Code? Inspecting Spark TransformationsBack to GlossaryProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/resources/analyst-paper/databricks-named-leader-by-gartner
Databricks Named a Leader | Databricks2022 Gartner® Magic Quadrant™Databricks Named a LeaderCloud Database Management SystemsDatabricks named a Leader in 2022 Gartner® Magic Quadrant™ CDBMSDatabricks is proud to announce that Gartner has named us a Leader in the 2022 Magic Quadrant for Cloud Database Management Systems for the second consecutive year.We believe this recognition validates our vision for the lakehouse as a single, unified platform for data management and engineering — as well as, analytics and AI.Download the report to learn why Gartner named Databricks a Leader and gain additional insight into the benefits that a lakehouse platform can bring to your organization.Access the Report Gartner, Magic Quadrant for Cloud Database Management Systems, Henry Cook, Merv Adrian, Rick Greenwald, Xingyu Gu, 13 December 2022. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Databricks.ProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/dataaisummit/speaker/eric-schmidt
Eric Schmidt - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingEric SchmidtFormer CEO and Chairman, Google; Co-Founder at Schmidt FuturesBack to speakersEric Schmidt is an accomplished technologist, entrepreneur and philanthropist. He joined Google in 2001 and helped grow the company from a Silicon Valley startup to a global leader in technology alongside founders Sergey Brin and Larry Page. Eric served as Google’s Chief Executive Officer and Chairman from 2001–2011, as well as Executive Chairman and Technical Advisor. Under his leadership, Google dramatically scaled its infrastructure and diversified its product offerings while maintaining a strong culture of innovation. In 2017, he co-founded Schmidt Futures, a philanthropic initiative that bets early on exceptional people making the world better. Eric founded the Special Competitive Studies Project in 2021, a nonprofit initiative focused on strengthening America’s long-term AI and technological competitiveness in national security, the economy and society.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/christian-hamilton
Christian Hamilton - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingChristian HamiltonDirector, Data Science Technology at 84.51Back to speakersChristian Hamilton is a Director of Data Science Technology at 84.51°. He has spent 22 years in Kroger companies, holding diverse titles in Data Science, Retail Operations, and Finance. His work in emerging technology includes developing the first recommender sciences for Kroger’s digital channels & implementing spark streaming. He’s currently focused on democratizing data across the enterprise, establishing single sources of truth, empowering collaboration, and championing observability and governance.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/company/newsroom/press-releases/databricks-partners-with-google-cloud-to-deliver-its-platform-to-global-businesses
Databricks Partners with Google Cloud to Deliver its Platform to Global BusinessesPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWDatabricks Partners with Google Cloud to Deliver its Platform to Global BusinessesDatabricks launches on Google Cloud with integrations to Google BigQuery and AI Platform that unify data engineering, data science, machine learning, and analytics across both companies’ services February 17, 2021Share this postSunnyvale and San Francisco, Calif., February 17, 2021 – Today, Google Cloud and Databricks announced a new partnership to deliver Databricks at global scale on Google Cloud. Under the partnership, organizations can now use Databricks to create a lakehouse capable of data engineering, data science, machine learning, and analytics on Google Cloud’s global, scalable, and elastic network. Databricks on Google Cloud will deeply integrate with Google BigQuery's open platform and will leverage Google Kubernetes Engine (GKE), enabling customers to deploy Databricks in a fully containerized cloud environment for the first time. With this integrated solution, organizations can unlock AI-driven insights, enable intelligent decision-making, and ultimately accelerate their digital transformations through data-driven applications. “Businesses with a strong foundation of data and analytics are well-positioned to grow and thrive in the next decade,” said Thomas Kurian, CEO at Google Cloud. “We’re delighted to deliver Databricks’ lakehouse for AI and ML-driven analytics on Google Cloud. By combining Databricks’ capabilities in data engineering and analytics with Google Cloud’s global, secure network—and our expertise in analytics and delivering containerized applications—we can help companies transform their businesses through the power of data.” “This is a pivotal milestone that underscores our commitment to enable customer flexibility and choice with a seamless experience across cloud platforms,” said Ali Ghodsi, CEO and co-founder of Databricks. “We are thrilled to partner with Google Cloud and deliver on our shared vision of a simplified, open, and unified data platform that supports all analytics and AI use-cases that will empower our customers to innovate even faster.” “For Condé Nast, finding valuable insights from massive amounts of data is essential for creating world class content experiences that delight our global customers. Databricks on Google Cloud provides a lakehouse for our data engineers, data scientists and analysts to consolidate, collaborate, analyze, and use all of our global data to experiment and innovate quickly. We’re excited to see leaders like Google Cloud and Databricks come together to streamline and simplify getting value from data,” said Nana Essuman, Director of Data Engineering at Condé Nast. Deploying Databricks rapidly and securely at global scale Global businesses need the ability to quickly deploy applications at any scale, and the elasticity to scale them up or down depending on their needs. Delivering Databricks on Google Cloud enables customers to rapidly provision Databricks on Google Cloud’s global network, with advanced security and data protection controls required for highly-regulated industries, and with the flexibility to quickly adjust usage based on the needs of the business. Additionally, customers will soon be able to deploy Databricks from the Google Cloud Marketplace, enabling simplified procurement and user provisioning, Single Sign-On, and unified billing. Advancing analytics with Databricks, BigQuery, and Google Cloud AI Platform Databricks on Google Cloud is tightly integrated with Google BigQuery, giving customers the freedom of choice and access to their choice of data analytics services. With this integration, businesses can extend their existing Databricks lakehouse capabilities, now running on Google Cloud, and can cross-leverage Google BigQuery for analytics, ultimately simplifying their data investments, increasing usage, and creating new, data-driven business models and opportunities. Unique integrations between Databricks and Google Cloud include: Tight integration of Databricks with Google Cloud’s analytics solutions, giving customers the ability to easily extend AI-driven insights across data lakes, data warehouses, and multiple business intelligence tools. Pre-built connectors to seamlessly and quickly integrate Databricks with BigQuery, Google Cloud Storage, Looker and Pub/Sub. Fast and scalable model training with AI Platform using the data workflows created in Databricks, and simplified deployment of models built in Databricks using AI Platform Prediction. Delivering containerized Databricks deployments for the first time Databricks on Google Cloud represents the first container-based deployments of Databricks, on any cloud. Increasingly, Kubernetes and containers are the de facto orchestration system for enterprise workloads and applications running in the cloud. Databricks on Google Cloud is built on GKE, Google Cloud’s secure, managed Kubernetes service, to support containerized deployments of Databricks in the cloud. By adopting GKE as an operating environment, Databricks is able to leverage managed services for security, network policy, and compute and as a result, provide customers with increasing business value through Databricks analytics, AI, and ML capabilities. Additionally, with GKE, Databricks increases its agility and the ability to accelerate the release of new features, quickly, at scale, and at lower cost. Supporting open source innovation and partner collaboration Databricks and Google Cloud share a commitment to open innovation and open source software. Under this new partnership, the two companies will continue to support the open source community, encourage open innovation and collaboration, making it easier for joint customers to build on open-source technologies. Additionally,  members of our joint ecosystem of partners have committed to ensure seamless integrations and expertise with Databricks on Google Cloud, including Accenture, Cognizant, Collibra, Confluent, Deloitte, Fishtown Analytics, Fivetran, Immuta, Informatica, Infoworks, Insight, MongoDB, Privacera, Qlik, SoftServe, Slalom, Tableau, TCS and Trifacta among others. On April 6, join Databricks CEO, Ali Ghodsi, and Google Cloud CEO, Thomas Kurian as they share more about the partnership and vision of an open, unified data analytics platform during a discussion hosted by TechCrunch; visit the event page for more information. To learn more about Databricks on Google Cloud, visit: https://www.databricks.com/product/google-cloud About Google Cloud Google Cloud provides organizations with leading infrastructure, platform capabilities and industry solutions. We deliver enterprise-grade cloud solutions that leverage Google’s cutting-edge technology to help companies operate more efficiently and adapt to changing needs, giving customers a foundation for the future. Customers in more than 150 countries turn to Google Cloud as their trusted partner to solve their most critical business problems. About Databricks Databricks is the data and AI company. Thousands of organizations worldwide — including Comcast, Condé Nast, Nationwide, and H&M — rely on Databricks’ open and unified platform for data engineering, data science, machine learning, and analytics. Databricks is venture-backed and headquartered in San Francisco, with offices around the globe. Founded by the original creators of Apache Spark™, Delta Lake, and MLflow, Databricks is on a mission to help data teams solve the world’s toughest problems. To learn more, follow Databricks on Twitter, LinkedIn, and Facebook. 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Data and AI Summit 2023 - DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingGeneration AILarge Language Models (LLM) are taking AI mainstream. Join the premier event for the global data community to understand their potential and shape the future of your industry with data and AI. Register NowSan Francisco, Moscone CenterJune 26 - 29, 2023Featured SpeakersTop experts, researchers and open source contributors from Databricks and across the data and AI community will speak at Data + AI Summit. Whether you’re an engineering wizard, ML pro, SQL expert — or you want to learn how to build, train and deploy LLMs — you’ll be in good company. See all speakersDaniela RusDirector, MIT CSAIL; Professor of EECS, MITPercy LiangProfessor of Computer Science, StanfordNat FriedmanCreator of Copilot; Former CEO, GitHubMichael CarbinCo-founder, MosaicML; Professor of EECS, MITKasey UhlenhuthStaff Product Manager, DatabricksWassym BensaidSr. Vice President, Software Development, RivianEric SchmidtCo-Founder, Schmidt Futures; Former CEO and Chairman, GoogleAdi PolakData & AI Technologist, lakeFSAli GhodsiCo-founder and CEO, DatabricksManu SharmaCEO, LabelboxMatei ZahariaOriginal Creator of Apache Spark™ and MLflow; Chief Technologist, DatabricksLin QiaoCo-creator of PyTorch, Co-founder and CEO, FireworksSai RavuruSenior Manager of Data Science & Analytics, Jet BlueEmad MostaqueCEO, Stability.AIHarrison ChaseCreator of LangChainSatya NadellaChairman and CEO, Microsoft, Live Virtual GuestZaheera ValaniSenior Director of Engineering, DatabricksHannes MühleisenCreator of DuckDBBrooke WenigMachine Learning Practice Lead, DatabricksJitendra MalikComputer Vision Pioneer, Former Head of Facebook AI ResearchRobin SutaraField CTO, DatabricksLior GavishCEO and Co-founder, Monte Carlo DataDawn SongProfessor of EECS, UC BerkeleyReynold XinCo-founder and Chief Architect, DatabricksDaniela RusDirector, MIT CSAIL; Professor of EECS, MITPercy LiangProfessor of Computer Science, StanfordNat FriedmanCreator of Copilot; Former CEO, GitHubMichael CarbinCo-founder, MosaicML; Professor of EECS, MITKasey UhlenhuthStaff Product Manager, DatabricksWassym BensaidSr. Vice President, Software Development, RivianEric SchmidtCo-Founder, Schmidt Futures; Former CEO and Chairman, GoogleAdi PolakData & AI Technologist, lakeFSAli GhodsiCo-founder and CEO, DatabricksManu SharmaCEO, LabelboxMatei ZahariaOriginal Creator of Apache Spark™ and MLflow; Chief Technologist, DatabricksLin QiaoCo-creator of PyTorch, Co-founder and CEO, FireworksSai RavuruSenior Manager of Data Science & Analytics, Jet BlueEmad MostaqueCEO, Stability.AIHarrison ChaseCreator of LangChainSatya NadellaChairman and CEO, Microsoft, Live Virtual GuestZaheera ValaniSenior Director of Engineering, DatabricksHannes MühleisenCreator of DuckDBBrooke WenigMachine Learning Practice Lead, DatabricksJitendra MalikComputer Vision Pioneer, Former Head of Facebook AI ResearchRobin SutaraField CTO, DatabricksLior GavishCEO and Co-founder, Monte Carlo DataDawn SongProfessor of EECS, UC BerkeleyReynold XinCo-founder and Chief Architect, DatabricksDaniela RusDirector, MIT CSAIL; Professor of EECS, MITPercy LiangProfessor of Computer Science, StanfordNat FriedmanCreator of Copilot; Former CEO, GitHubMichael CarbinCo-founder, MosaicML; Professor of EECS, MITKasey UhlenhuthStaff Product Manager, DatabricksWassym BensaidSr. Vice President, Software Development, RivianEric SchmidtCo-Founder, Schmidt Futures; Former CEO and Chairman, GoogleWhy attend?Join thousands of data leaders, engineers, scientists and analysts to explore all things data, analytics and AI — and how these are unified on the lakehouse. Hear from the data teams who are transforming their industries. Learn how to build and apply LLMs to your business. Uplevel your skills with hands-on training and role-based certifications. Connect with data professionals from around the world and learn more about all Data + AI Summit has to offer. SessionsWith more than 250 sessions, Data + AI Summit has something for everyone. Choose from technical deep dives, hands-on training, lightning talks, industry sessions, and more. Explore sessionsTechnologyExplore the latest advances in leading open source projects and industry technologies like Apache Spark™, Delta Lake, MLflow, Dolly, PyTorch, dbt, Presto/Trino, DuckDB and much more. You’ll also get a first look at new products and features in the Databricks Lakehouse Platform. Browse catalogNetworkingConnect with thousands of data + AI community peers and grow your professional network in social meetups, on the expo floor, or at our event party. Learn moreChoose your experienceGet access to all the sessions, training, and special events live in San Francisco or join us virtually for the keynotes.RECOMMENDEDActivitiesIn Person EventVirtual EventKeynotesBreakout Sessions300+10Hands-on Training Courses for Data Engineering, Machine Learning, LLMs, and many onsite certificationsConnect with other data pros at “birds of a feather” meals, happy hours and special eventsLightning talks, AMAs and meetups on topics such as Apache Spark™, Delta Lake, MLflow and DollyAccess to 100+ leading data and AI companies in Dev Hub + ExpoIndustry Forums for Financial Services, Retail and Consumer Goods, Healthcare and Life Sciences, Communications, Media and Entertainment, Public Sector, and Manufacturing and EnergySee pricingTrusted by the data communityHear data practitioners from trusted companies all over the world See agendaDon’t miss this year’s event!Register nowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. 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データレイクハウスアーキテクチャと AI の企業 ― DatabricksSkip to main contentプラットフォームデータブリックスのレイクハウスプラットフォームDelta Lakeデータガバナンスデータエンジニアリングデータストリーミングデータウェアハウスデータ共有機械学習データサイエンス料金Marketplaceオープンソーステクノロジーセキュリティ&トラストセンターウェビナー 5 月 18 日午前 8 時 PT さようなら、データウェアハウス。こんにちは、レイクハウス。 データレイクハウスが最新のデータスタックにどのように適合するかを理解するために出席してください。 今すぐ登録ソリューション業種別のソリューション金融サービス医療・ライフサイエンス製造通信、メディア・エンターテイメント公共機関小売・消費財全ての業界を見るユースケース別ソリューションソリューションアクセラレータプロフェッショナルサービスデジタルネイティブビジネスデータプラットフォームの移行5月9日 |午前8時(太平洋標準時)   製造業のためのレイクハウスを発見する コーニングが、手作業による検査を最小限に抑え、輸送コストを削減し、顧客満足度を高める重要な意思決定をどのように行っているかをご覧ください。今すぐ登録学習ドキュメントトレーニング・認定デモ関連リソースオンラインコミュニティ大学との連携イベントDATA+AI サミットブログラボBeacons2023年6月26日~29日 直接参加するか、基調講演のライブストリームに参加してくださいご登録導入事例パートナークラウドパートナーAWSAzureGoogle CloudPartner Connect技術・データパートナー技術パートナープログラムデータパートナープログラムBuilt on Databricks Partner ProgramSI コンサルティングパートナーC&SI パートナーパートナーソリューションDatabricks 認定のパートナーソリューションをご利用いただけます。詳しく見る会社情報採用情報経営陣取締役会Databricks ブログニュースルームDatabricks Ventures受賞歴と業界評価ご相談・お問い合わせDatabricks は、ガートナーのマジック・クアドラントで 2 年連続でリーダーに位置付けられています。レポートをダウンロードDatabricks 無料トライアルデモを見るご相談・お問い合わせログインJUNE 26-29REGISTER NOW最良の データウェアハウス レイクハウスでデータ、分析、AI を 1 つのプラットフォームで一元化無料トライアルを開始詳しく見るレイクハウスプラットフォームでコストを削減し、イノベーションを加速詳しく見る統合単一のプラットフォームでのデータ管理を可能にし、あらゆる分析と AI に対応オープンオープンスタンダードを基盤とし、あらゆるクラウドとの統合により、モダンデータスタックのシームレスな動作を可能にスケーラビリティシンプルなデータパイプラインから大規模な LLM まで、あらゆるワークロードを効率的にスケールアップデータドリブンな組織に選ばれる「レイクハウス」 導入事例一覧へ レイクハウスがデータチームをひとつにデータ管理とエンジニアリングデータの取り込みと 管理の効率化Delta Lake は、自動化された信頼性の高い ETL、オープンでセキュアなデータ共有、超高速性能を備え、構造化/半構造化/非構造化のあらゆるデータを任意のデータレイクに格納します。詳しく見る デモ動画を見るデータウェアハウス完全なデータから 新たな知見を引き出すDatabricks SQL は、従来のクラウド型のデータウェアハウスの性能と比較して最大 12 倍の価格性能で、最新で完全なデータへの容易なアクセスを実現。データアナリストやデータサイエンティストが新たな知見を迅速に引き出すことを可能にします。詳しく見る デモ動画を見るデータサイエンスと機械学習一連の ML プロセスを加速Databricks の機械学習はレイクハウスを基盤として構築されています。データネイティブなコラボレーション型のソリューションにより、特徴量の生成から本番環境に至るまで、機械学習の完全なライフサイクルをサポートします。高品質、高性能のデータパイプラインとの相乗効果により、レイクハウスは、機械学習を効率化し、データチームの生産性を向上させます。詳しく見る デモ動画を見るデータの共有とガバナンスデータ・分析・AI の共有とガバナンスを一元化Databricks では、あらゆるクラウド上のレイクハウスのデータ、分析、AI のアセットに対して共通のセキュリティ/ガバナンスモデルを適用できます。データプラットフォーム、クラウド、リージョンを問わず、データの発見・共有が可能です。レプリケーションは不要で、ロックインもありません。データプロダクトは、マーケットプレイスを介して配布できます。詳しく見る デモ動画を見るデータウェアハウスからレイクハウスへ ― データと AI のためのモダンアーキテクチャレイクハウスによる革新セッションのカタログを公開しましたサンフランシスコで開催されるサミットで、レイクハウスのエコシステムとオープンソースの技術の進歩の詳細をご覧いただけます。ご登録Databricks は、ガートナーの「クラウドデータベース管理システム部門のマジック・クアドラント」において 2 年連続でリーダーの 1 社として位置付けられています。レポートをダウンロード18 か国、14 の業界、600 名の CIO の意識調査AI の成功にはデータ戦略が不可欠であることが最新の調査で明らかになっています。レポートでは、CIO の視点を掘り下げて解説しています。レポートをダウンロードMLOps の改善を追求する ML エンジニアやデータサイエンティスト向けに MLOps のベストプラクティスを解説しています。eBook をダウンロード無料お試し・その他ご相談を承りますDatabricks 無料トライアル製品プラットフォーム料金オープンソーステクノロジーDatabricks 無料トライアルデモ製品プラットフォーム料金オープンソーステクノロジーDatabricks 無料トライアルデモ学習・サポートドキュメント用語集トレーニング・認定ヘルプセンター法務オンラインコミュニティ学習・サポートドキュメント用語集トレーニング・認定ヘルプセンター法務オンラインコミュニティソリューション業種別プロフェッショナルサービスソリューション業種別プロフェッショナルサービス会社情報会社概要採用情報ダイバーシティ&インクルージョンDatabricks ブログご相談・お問い合わせ会社情報会社概要採用情報ダイバーシティ&インクルージョンDatabricks ブログご相談・お問い合わせ採用情報言語地域English (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.プライバシー通知|利用規約|プライバシー設定|カリフォルニア州のプライバシー権利
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Databricks documentation | Databricks on AWS Support Feedback Try Databricks Help Center Documentation Knowledge Base Amazon Web Services Microsoft Azure Google Cloud Platform Databricks on AWS Get started Get started What is Databricks? Tutorials and best practices Release notes Load & manage data Load data Explore data Prepare data Share data (Delta sharing) Data Marketplace Work with data Data engineering Machine learning Data warehousing Delta Lake Developer tools Technology partners Administration Account and workspace administration Security and compliance Data governance Lakehouse architecture Reference & resources Reference Resources What’s coming? Documentation archive Updated May 10, 2023 Send us feedback Documentation Databricks documentation Databricks documentation Databricks documentation provides how-to guidance and reference information for data analysts, data scientists, and data engineers working in the Databricks Data Science & Engineering, Databricks Machine Learning, and Databricks SQL environments. The Databricks Lakehouse Platform enables data teams to collaborate. Try Databricks Get a free trial & set up Query data from a notebook Build a basic ETL pipeline Build a simple Lakehouse analytics pipeline Free training What do you want to do? Data science & engineering Machine learning SQL queries & visualizations Manage Databricks Account & workspace administration Security & compliance Data governance Reference Guides API reference SQL language reference Error messages Resources Release notes Other resources © Databricks 2023. All rights reserved. Apache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. Send us feedback | Privacy Policy | Terms of Use
https://www.databricks.com/dataaisummit/speaker/jonathan-keller
Jonathan Keller - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingJonathan KellerSr. Director, Product Management at DatabricksBack to speakersJonathan leads Product Management for data governance at Databricks, including Unity Catalog and Delta Sharing. He was previously Director of Product Management for Google Cloud’s BigQuery team, after almost 20 years at Microsoft in various roles.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/blog/2022/06/28/project-lightspeed-faster-and-simpler-stream-processing-with-apache-spark.html
Project Lightspeed: Faster and Simpler Stream Processing With Apache SparkSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCategoriesAll blog postsCompanyCultureCustomersEventsNewsPlatformAnnouncementsPartnersProductSolutionsSecurity and TrustEngineeringData Science and MLOpen SourceSolutions AcceleratorsData EngineeringTutorialsData StreamingData WarehousingData StrategyBest PracticesData LeaderInsightsIndustriesFinancial ServicesHealth and Life SciencesMedia and EntertainmentRetailManufacturingPublic SectorProject Lightspeed: Faster and Simpler Stream Processing With Apache Sparkby Karthik Ramasamy, Matei Zaharia, Reynold Xin, Michael Armbrust, Awez Syed, Ray Zhu, Alexander Balikov, Jerry Peng, Shrikanth Shankar and Sameer ParanjpyeJune 28, 2022 in Engineering BlogShare this postStreaming data is a critical area of computing today. It is the basis for making quick decisions on the enormous amounts of incoming data that systems generate, whether web postings, sales feeds, or sensor data, etc. Processing streaming data is also technically challenging, and it has needs far different from and more complicated to meet than those of event-driven applications and batch processing.To meet the stream processing needs, Structured Streaming was introduced in Apache Spark™ 2.0. Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. The user can express the logic using SQL or Dataset/DataFrame API. The engine will take care of running the pipeline incrementally and continuously and update the final result as streaming data continues to arrive. Structured Streaming has been the mainstay for several years and is widely adopted across 1000s of organizations, processing more than 1 PB of data (compressed) per day on the Databricks platform alone.As the adoption accelerated and the diversity of applications moving into streaming increased, new requirements emerged. We are starting a new initiative codenamed Project Lightspeed to meet these requirements, which will take Spark Structured Streaming to the next generation. The requirements addressed by Lightspeed are bucketed into four distinct categories:Improving the latency and ensuring it is predictableEnhancing functionality for processing data with new operators and APIsImproving ecosystem support for connectorsSimplifying deployment, operations, monitoring and troubleshootingIn this blog, we will discuss the growth of Spark Structured Streaming and its key benefits. Then we will outline an overview of the proposed new features and functionality in Project Lightspeed.Growth of Spark Structured StreamingSpark Structured Streaming has been widely adopted since the early days of streaming because of its ease of use, performance, large ecosystem, and developer communities. The majority of streaming workloads we saw were customers migrating their batch workloads to take advantage of the lower latency, fault tolerance, and support for incremental processing that streaming offers. We have seen tremendous adoption from streaming customers for both open source Spark and Databricks. The graph below shows the weekly number of streaming jobs on Databricks over the past three years, which has grown from thousands to 4+ millions and is still accelerating.Advantages of Spark Structured StreamingSeveral properties of Structured Streaming have made it popular for thousands of streaming applications today.Unification - The foremost advantage of Structured Streaming is that it uses the same API as batch processing in Spark DataFrames, making the transition to real-time processing from batch much simpler. Users can simply write a DataFrame computation using Python, SQL, or Spark’s other supported languages and ask the engine to run it as an incremental streaming application. The computation will then run incrementally as new data arrives, and recover automatically from failures with exactly-once semantics, while running through the same engine implementation as a batch computation and thus giving consistent results. Such sharing reduces complexity, eliminates the possibility of divergence between batch and streaming workloads, and lowers the cost of operations (consolidation of infrastructure is a key benefit of Lakehouse). Additionally, many of Spark’s other built-in libraries can be called in a streaming context, including ML libraries.Fault Tolerance & Recovery - Structured Streaming checkpoints state automatically during processing. When a failure occurs, it automatically recovers from the previous state. The failure recovery is very fast since it is restricted to failed tasks as opposed to restarting the entire streaming pipeline in other systems. Furthermore, fault tolerance using replayable sources and idempotent sinks enables end-to-end exactly-once semantics.Performance - Structured Streaming provides very high throughput with seconds of latency at a lower cost, taking full advantage of the performance optimizations in the Spark SQL engine. The system can also adjust itself based on the resources provided thereby trading off cost, throughput and latency and supporting dynamic scaling of a running cluster. This is in contrast to systems that require upfront commitment of resources.Flexible Operations - The ability to apply arbitrary logic and operations on the output of a streaming query using foreachBatch, enabling the ability to perform operations like upserts, writes to multiple sinks, and interact with external data sources. Over 40% of our users on Databricks take advantage of this feature.Stateful Processing - Support for stateful aggregations and joins along with watermarks for bounded state and late order processing. In addition, arbitrary stateful operations with [flat]mapGroupsWithState backed by a RocksDB state store are provided for efficient and fault-tolerant state management (as of Spark 3.2).Project LightspeedWith the significant growing interest in streaming in enterprises and making Spark Structured Streaming the de facto standard across a wide variety of applications, Project Lightspeed will be heavily investing in improving the following areas:Predictable Low LatencyApache Spark Structured Streaming provides a balanced performance across multiple dimensions - throughput, latency and cost. As Structured Streaming grew and is used in new applications, we are profiling our customer workloads to guide improvements in tail latency by up to 2x. Towards meeting this goal, some of the initiatives we will be undertaking are as follows:Offset Management - Our customer workload profiling and performance experiments indicate that offset management operations consume upto 30-50% of the time for pipelines. This can be improved by making these operations asynchronous and configurable cadence, thereby reducing the latency.Asynchronous Checkpointing - Current checkpointing mechanism synchronously writes into object storage after processing a group of records. This contributes substantially to latency. This could be improved by as much as 25% by overlapping the execution of the next group of records with writing of the checkpointing for the previous group of records.State Checkpointing Frequency - Spark Structured Streaming checkpoints the state after a group of records have been processed that adds to end-to-end latency. Instead, if we make it tunable to checkpoint every Nth group, the latency can be further reduced depending on the choice for N.Enhanced Functionality for Processing Data / EventsSpark Structured Streaming already has rich functionality for expressing predominant sets of use cases. As enterprises extend streaming into new use cases, additional functionality is needed to express them concisely. Project Lightspeed is advancing the functionality in the following areas:Multiple Stateful Operators - Currently, Structured Streaming supports only one stateful operator per streaming job. However, some use cases require multiple state operators in a job such as:  Chained time window aggregation (e.g. 5 mins tumble window aggregation followed by 1 hour tumble window aggregation)Chained stream-stream outer equality join (e.g. A left outer join B left outer join C)Stream-stream time interval join followed by time window aggregationProject Lightspeed will add support for this capability with consistent semantics.Advanced Windowing - Spark Structured Streaming provides basic windowing that addresses most use cases. Advanced windowing will augment this functionality with simple, easy to use, and intuitive API to support arbitrary groups of window elements, define generic processing logic over the window, ability to describe when to trigger the processing logic and the option to evict window elements before or after the processing logic is applied.State Management - Stateful support is provided through predefined aggregators and joins. In addition, specialized APIs are provided for direct access to state and manipulating it. New functionality, in Lightspeed, will incorporate the evolution of the state schema as the processing logic changes and the ability to query the state externally.Asynchronous I/O - Often, in ETL, there is a need to join a stream with external databases and microservices. Project Lightspeed will introduce a new API that manages connections to external systems, batch requests for efficiency and handles them asynchronously.Python API Parity - While Python API is popular, it still lacks the primitives for stateful processing. Lightspeed will add a powerful yet simple API for storing and manipulating state. Furthermore, Lightspeed will provide tighter integrations with popular Python data processing packages like Pandas - to make it easy for the developers.Connectors and EcosystemConnectors make it easier to use the Spark Structured Streaming engine to process data from and write processed data into various messaging buses like Apache Kafka and storage systems like Delta lake. As part of Project Lightspeed, we will work on the following:New Connectors - We will add new connectors working with partners (for example, Google Pub/Sub, Amazon DynamoDB) to enable developers to easily use the Spark Structured Streaming engine with additional messaging buses and storage systems they prefer.Connector Enhancement - We will enable new functionalities and improve performance on existing connectors. Some examples include AWS IAM auth support in the Apache Kafka connector and enhanced fan-out support in the Amazon Kinesis connector.Operations and TroubleshootingStructured Streaming jobs are continuously running until explicitly terminated. Because of the always-on nature, it is necessary to have the appropriate tools and metrics to monitor, debug and alert when certain thresholds are exceeded. Towards satisfying these goals, Project Lightspeed will improve the following:Observability - Currently, the metrics generated from structured streaming pipelines for monitoring require coding to collect and visualize. We will unify the metric collection mechanism and provide capabilities to export to different systems and formats. Furthermore, based on customer input, we will add additional metrics for troubleshooting.Debuggability - We will provide capabilities to visualize pipelines and how its operators are grouped and mapped into tasks and the executors the tasks are running. Furthermore, we will implement the ability to drill down to specific executors, browse their logs and various metrics.What’s NextIn this blog, we discussed the advantages of Spark Structured Streaming and how it contributed to its widespread growth and adoption. We introduced Project Lightspeed which advances Spark Structured Streaming into the real-time era as more and more new use cases and workloads migrate into streaming.In subsequent blogs, we will expand on the individual categories of improving Spark Structured Streaming performance across multiple dimensions, enhanced functionality, operations and ecosystem support.Project Lightspeed will roll out incrementally by collaborating and closely working with community. We are expecting most of the features to be delivered by early next year.Try Databricks for freeGet StartedRelated postsStructured Streaming: A Year in ReviewFebruary 7, 2022 by Steven Yu and Ray Zhu in Data Engineering As we enter 2022, we want to take a moment to reflect on the great strides made on the streaming front in Databricks... How to Monitor Streaming Queries in PySparkMay 27, 2022 by Hyukjin Kwon, Karthik Ramasamy and Alexander Balikov in Open Source Streaming is one of the most important data processing techniques for ingestion and analysis. It provides users and developers with low latency and... What’s New in Apache Spark™ 3.1 Release for Structured StreamingApril 27, 2021 by Yuanjian Li, Shixiong Zhu and Bo Zhang in Engineering Blog Along with providing the ability for streaming processing based on Spark Core and SQL API, Structured Streaming is one of the most important... See all Engineering Blog postsProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. 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https://www.databricks.com/solutions/accelerators/retention-management
Solution Accelerator - How to build: Profit-driven retention management | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWSolution AcceleratorHow to build: Profit-driven retention managementEffectively manage retention and reduce churn Develop an understanding of how a customer lifetime should progress and examine where in that lifetime journey customers are likely to churn so you can effectively manage retention and reduce your churn rate. Read the full write-up Download notebooksBenefits and business valueStop the bleedingIdentify which customers are most likely to leave your service Massive speed improvementScan through large volumes of data easily, and quickly generate useful customer analysis records Increase customer lifetime valueIdentify the value of retaining the customer Reference ArchitectureDeliver AI innovation faster with Solution Accelerators for popular industry use cases. See our full library of solutionsReady to get started?Try Databricks for freeProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/company/careers/open-positions
Current job openings at Databricks | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOW OverviewCultureBenefitsDiversityStudents & new gradsCurrent job openings at DatabricksDepartmentLocationProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/dataaisummit/speaker/francisco-rius/#
Francisco Rius - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFrancisco RiusHead of Data Science and Data Engineering at Minecraft at MicrosoftBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/junta-nakai
Junta Nakai - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingJunta NakaiRVP, Industry Solutions, Financial Services and Sustainability at DatabricksBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/christopher-locklin
Christopher Locklin - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingChristopher LocklinEngineer Manager, Data Platform at GrammarlyBack to speakersChris Locklin has been leading data infrastructure and engineering teams for the last 9 years (at Grammarly, Dropbox, and Verizon), and initially entered the data space in 2010. He is currently the engineering manager of the Data Platform team at Grammarly. The team is responsible for ingesting, processing, and surfacing over 50 billion events every day.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
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https://www.databricks.com/discover/demos/delta-live-tables-demo
Delta Live Tables Demo: Reliable Data Pipelines | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWDelta Live Tables DemosGet started for freeDelta Live Tables Overview In this demo, we give you a first look at Delta Live Tables, a cloud service that makes reliable ETL – extract, transform and load capabilities – easy on Delta Lake. It helps data engineering teams streamline ETL development with a simple UI and declarative tooling, improve data reliability through defined data quality rules and bad data monitoring, and scale operations with deep visibility through an event log. Streaming Data With Delta Live Tables Let's analyze tweets from Data + AI Summit 2022! Modern data engineering requires a more advanced data lifecycle for data ingestion, transformation and processing. In this session, you can learn how the Databricks Lakehouse Platform provides an end-to-end data engineering solution that automates the complexity of building and maintaining data pipelines. Enjoy a fun, live, streaming data example with a Twitter data stream, Databricks Auto Loader and Delta Live Tables as well as Hugging Face sentiment analysis. How to Create Low Latency Streaming Data Pipelines With Apache Kafka or Amazon Kinesis and Delta Live Tables As shown at the Current.io 2022 conference in Austin (the next generation of Kafka Summit), this live demo elaborates on how the Databricks Lakehouse Platform simplifies data streaming to deliver streaming analytics and applications on one platform. Learn how to build low latency streaming data pipelines that ingest from a message bus like Confluent Cloud, Apache Kafka or any other Kafka-compatible cloud service such as Amazon MSK. The same principles can be used to ingest data from Amazon Kinesis. Frank's full conference session spans Spark on Databricks, Spark Structured Streaming with Delta Lake, and Delta Live Tables. Slides, code and a blog posting are available. See full list of demosDive deeper into Delta LakeLearn moreLearn moreLearn moreCreate your Databricks account1/2First nameLast NameEmailCompanyTitlePhone (Optional)SelectCountryGet Started for freeReady to get started?Try Databricks for freeProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/explore/de-data-warehousing/oreilly-definitive-guide
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https://www.databricks.com/p/webinar/data-management-the-good-the-bad-the-ugly?itm_data=product-page-resource-data-mamagemet-big-rock
Data Management The Good The Bad The Ugly - DatabricksOn-Demand WebinarData ManagementThe good, the bad, the uglyCould this be the end of the 2 AM page? Learn how Databricks helps data engineers sleep at night.Maintaining data infrastructure isn’t easy, but it’s the foundation that makes ML, AI and data science possible. Discover how Databricks simplifies data management — from data processing with ETL to data governance — and why that makes the lakehouse architecture a reality.You’ll also learn about newly released features and tools that extend the power of the Databricks Lakehouse Platform. Here’s what we’ll cover:How to automatically and reliably ingest and prepare structured and unstructured data at scale for data lakesHow to simplify your architecture and enable data scientists and analysts to query the freshest and most complete data using their SQL and BI tools of choiceHow to centrally share and govern data within and across organizations using open source Delta Sharing and a unified data catalogThis presentation will come to life with an end-to-end demo of Databricks’ most recent capabilities for end-to-end data management and a live Q&A throughout the event.The notebooks will be provided so you can follow along and practice at your own pace.Watch NowProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
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Email Protection | Cloudflare Please enable cookies. Email Protection You are unable to access this email address databricks.com The website from which you got to this page is protected by Cloudflare. Email addresses on that page have been hidden in order to keep them from being accessed by malicious bots. You must enable Javascript in your browser in order to decode the e-mail address. If you have a website and are interested in protecting it in a similar way, you can sign up for Cloudflare. How does Cloudflare protect email addresses on website from spammers? Can I sign up for Cloudflare? Cloudflare Ray ID: 7c5c2dfb783c3adc • Your IP: Click to reveal 2601:147:4700:3180:15eb:de93:22f5:f511 • Performance & security by Cloudflare
https://www.databricks.com/dataaisummit/speaker/deepa-paranjpe
Deepa Paranjpe - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingDeepa ParanjpeDirector of Engineering at DiscoveryBack to speakersDeepa Paranjpe is director of engineering in WBD.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/himanshu-arora/#
Himanshu Arora - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingHimanshu AroraResident Solutions Architect at DatabricksBack to speakersHimanshu is a Resident Solutions Architect at Databricks. He has been using Spark, Big Data, and Reactive systems for a few years now in production to help enterprises in their data and digital transformation journey. Prior to that, he was a Java/Scala developer. Himanshu joined Databricks in 2020 and since then he has been helping Databricks customers on numerous projects around Data Architecture & Design, Optimization & Best Practices, Lakehouse implementation, Migration to Databricks, etc.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/wp-content/uploads/2022/08/Databricks-Modern-Investment-Data-Platforms-with-Lakehouse-for-Financial-Services-Solution-Sheet-082322.pdf
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https://www.databricks.com/glossary/sparklyr
What is Sparklyr?PlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWSparklyrAll>SparklyrTry Databricks for freeGet StartedWhat is Sparklyr?Sparklyr is an open-source package that provides an interface between R and Apache Spark. You can now leverage Spark’s capabilities in a modern R environment, due to Spark’s ability to interact with distributed data with little latency. Sparklyr is an effective tool for interfacing with large datasets in an interactive environment. This way you can benefit from the familiar tools in R in order to analyze data in Spark., giving you the best of both worlds. Through Sparklyr you can use Spark as the backend for dplyr, a popular data manipulation package. Sparklyr provides a range of functions that allow us to access the Spark tools for transforming/pre-processing data, On top of that, it also provides interfaces to Spark’s distributed machine learning algorithms and much more. Sparklyr is also extensible. R packages that depend on Sparklyr to call the full Spark API can be created. One such extension is H2O’s Rsparkling, an R package compatible with H2O’s machine learning algorithm.Main highlights of Sparklyr:Users can interactively manipulate Spark data using dplyr as well as SQL (via DBI).Spark datasets can be filtered and aggregated and afterward brought into R to be analyzed.You will be able to orchestrate distributed machine learning from R using either Spark MLlib or H2O SparkingWater.Sparklyr users are able to generate extensions that call the full Spark API and provide interfaces to Spark packages.Sparklyr tool offers an exhaustive dplyr backend useful in case of data manipulation, analysis, and visualizationLoads data into Spark DataFrames from various locations such as local R data frames, Hive tables, CSV, JSON, and Parquet files.Sparklyr is able to connect to both local instances of Spark as well as to remote Spark clustersAdditional ResourcesSparklyr DocumentationR and Spark: How to Analyze Data Using RStudio’s Sparklyr and H2O’s Rsparkling PackagesIntroducing Apache Spark 3.0: Now available in Databricks Runtime 7.0Back to GlossaryProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/explore/media-entertainment-resources/explore-lakehouse-for-media
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https://www.databricks.com/glossary/artificial-neural-network
What is an Artificial Neural Network?PlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWArtificial Neural NetworkAll>Artificial Neural NetworkTry Databricks for freeGet StartedWhat is an Artificial Neural Network?An artificial neuron network (ANN) is a computing system patterned after the operation of neurons in the human brain.How Do Artificial Neural Networks Work?Artificial Neural Networks can be best viewed as weighted directed graphs, that are commonly organized in layers. These layers feature many nodes which imitate biological neurons of the human brain. that are interconnected and contain an activation function. The first layer receives the raw input signal from the external world-- analogous to optic nerves in human visual processing. Each successive layer gets the output from the layer preceding it, similar to the way neurons that are situated further from the optic nerve receive signals from those closest to them. The output at each node is called its activation or node value. The last tier produces the output of the system. ANNs are actually mathematical models that are capable of learning; by using ANNs we have been able to enhance existing data analysis technologies. They are one of the reasons we have seen an important progress in artificial intelligence (AI), machine learning (ML), and deep learning.Perceptron Artificial Neural NetworkPerceptron is the simplest type of artificial neural network. This type of network is typically used for making binary predictions. A Perceptron can only work if the data can be linearly separable. Multi-layer Artificial Neural NetworkA fully connected multi-layer neural network is also known as a Multilayer Perceptron (MLP). This type of artificial neural network is made of more than one layer of artificial neurons or nodes, ( for example the Convolutional Neural Network, Recurrent Neural Network etc …) A multi-layer ANN is used to solve more complex classification and regression tasks. The most common model is the 3-layer fully-connected backpropagation model. The first layer consists of input neurons, that send data on to the second layer, which in turn sends the output neurons to the third layer. Furthermore, there are two Artificial Neural Network topologies: FeedForward and Feedback.FeedForward Artificial Neural NetworkIn this ANN, the information flow is unidirectional. The information travels only in one direction; forward; without making any feedback loops.  It first goes through the input nodes, then through the hidden nodes (in case there are any), and in the end, it goes through the output nodes.FeedBack Artificial Neural NetworkIn this case, there are inherent feedback connections between the neurons of the networks. Here, feedback loops are allowed.  Additional ResourcesIntroduction to Neural Networks WebinarTraining your Neural Network WebinarApplying your Convolutional Neural Network WebinarBack to GlossaryProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/dataaisummit/speaker/ricardo-portilla
Ricardo Portilla - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingRicardo PortillaPrincipal Solution Architect at DatabricksBack to speakersRicardo has 12+ years of experience with Financial Services customers bringing use cases to production. He has designed and consulted on solutions architecture with dozens of customers from Capital Markets to Banking and Wealth Management. His previous work was at FINRA where he moved FINRA mission-critical workloads from on-prem warehouses to the cloud and established a machine learning practice for financial fraudLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
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다양성과 포용성 | DatabricksSkip to main content플랫폼Databricks 레이크하우스 플랫폼Delta Lake데이터 거버넌스데이터 엔지니어링데이터 스트리밍데이터 웨어하우징데이터 공유머신 러닝데이터 사이언스가격Marketplace오픈 소스 기술보안 및 신뢰 센터웨비나 5월 18일 / 오전 8시(태평양 표준시) 안녕, 데이터 웨어하우스. 안녕하세요, 레이크하우스입니다. 데이터 레이크하우스가 최신 데이터 스택에 어떻게 부합하는지 이해하려면 참석하십시오. 지금 등록하세요솔루션산업별 솔루션금융 서비스의료 서비스 및 생명 공학제조커뮤니케이션, 미디어 및 엔터테인먼트공공 부문리테일모든 산업 보기사용 사례별 솔루션솔루션 액셀러레이터프로페셔널 서비스디지털 네이티브 비즈니스데이터 플랫폼 마이그레이션5월 9일 | 오전 8시(태평양 표준시)   제조업을 위한 레이크하우스 살펴보기 코닝이 수동 검사를 최소화하고 운송 비용을 절감하며 고객 만족도를 높이는 중요한 결정을 내리는 방법을 들어보십시오.지금 등록하세요학습관련 문서교육 및 인증데모리소스온라인 커뮤니티University Alliance이벤트Data + AI Summit블로그LabsBeacons2023년 6월 26일~29일 직접 참석하거나 키노트 라이브스트림을 시청하세요.지금 등록하기고객파트너클라우드 파트너AWSAzureGoogle CloudPartner Connect기술 및 데이터 파트너기술 파트너 프로그램데이터 파트너 프로그램Built on Databricks Partner Program컨설팅 & SI 파트너C&SI 파트너 프로그램파트너 솔루션클릭 몇 번만으로 검증된 파트너 솔루션과 연결됩니다.자세히회사Databricks 채용Databricks 팀이사회회사 블로그보도 자료Databricks 벤처수상 실적문의처Gartner가 Databricks를 2년 연속 리더로 선정한 이유 알아보기보고서 받기Databricks 이용해 보기데모 보기문의처로그인JUNE 26-29REGISTER NOW미래의 데이터는 모두에게 열려 있습니다Databricks의 다양성, 평등 및 포용성데이터로 움직이고, 사람이 지원합니다.저희는 빅데이터를 다양화하는 것을 목표로 합니다. 그 노력은 우리 팀부터 솔선수범합니다. 다양한 배경, 경험, 관점, 인사이트, 기술은 혁신을 촉진하고, 서로 간의 유대와 고객과의 유대를 더욱 깊어지게 합니다. 저희는 모든 사람이 자신의 커리어에서 최고의 성과를 낼 수 있는 소속감을 키우는 문화를 조성하고자 합니다. 동일 노동 동일 임금 원칙을 준수하는 것에서부터 우리 팀의 노고를 축하하고, 팀원들을 교육하고 사기를 높이는 프로그램을 만드는 것에 이르기까지, DEI(Diversity, Equity and Inclusion)는 저희가 하는 모든 일에 바탕이 됩니다.Fair Pay Workplace 인증 Fair Pay Workplace에서 처음으로 인증을 받은 6개 기관 중 하나인 것에 자부심을 느끼빈다. Databricks에서는 공평한 급여를 제공하기 위한 노력의 일환으로, 급여 데이터와 제도를 엄격히 평가하며 지속적인 급여 평등성 분석을 통해 끝까지 책임을 지고자 합니다.Gaingels 및 Flucas Ventures 다양성, 평등, 포용성에 대한 노력은 저희와 함께 일하는 사람들에서부터 저희에게 투자하는 사람들에 이르기까지 우리 팀에 참여하는 모든 사람을 아우릅니다. 그래서 DEI에 대한 깊은 헌신을 보이는 기업에 투자하는 Gaingels, Flucas Ventures 등의 세계적 수준의 여러 투자자와도 파트너를 맺은 것을 자랑스럽게 생각합니다.“데이터가 많을수록 더 좋은 인사이트를 얻습니다. 다양성과 포용성도 마찬가지입니다. Databricks는 배경, 경험, 관점을 최대한 다양하게 포용함으로써 번창합니다.”— Ali Ghodsi, 공동 창업자 겸 CEO, Databricks우리 커뮤니티직원을 지원하는 것이야말로 우리 회사의 모든 잠재력을 실현할 수 있는 열쇠라고 생각합니다. 활기 넘치는 직원 리소스 그룹(ERG)는 직원이 주도하는 공동체로, Databricks에서 포용적이고 힘이 되는 환경을 조성하는 데 중요한 역할을 합니다. 직원과 협력자로 구성된 이런 다양한 커뮤니티는 유대를 키우고 서로 축하하기 위한 공간을 마련해주며, 의미 있는 문제에 대한 의식을 제고합니다.Queeries Network Veterans Network Black Employee Network Women’s Network LatinX Network Asian Employee Network 직원 경험“저는 Databricks에서 제가 속하고 싶은 팀을 구축합니다. 즉, 진정한 제 자신을 안전하게 공개할 수 있고 주변의 모든 사람들에게도 똑같이 편안한 공간을 제공하는 것입니다. 우리가 서로에게 보여주는 배려와 포용에 대한 헌신은 우리 커뮤니티 전체에 엄청난 영향을 미칩니다.”— Stacy Kerkela, 엔지니어링 이사자세히 읽기“Databricks의 LatinX Network에서 개방적 커뮤니티에 대한 강한 소속감을 느낍니다. Databricks의 직원 리소스 그룹에서는 배경과 관계없이 모든 사람을 환영하고 교육하기 때문입니다.”— Miguel Peralvo, 선임 솔루션 아키텍트자세히 읽기“많은 기업이 성 다양성을 늘리는 데 집중하지만, 큰 변화를 일으키기 위해 가시적인 조치를 취하는 회사에 소속되어 있는 것을 기쁘게 생각합니다.”— Allie Emrich, 제품 프로그램 관리자자세히 읽기포용 활동보고서ColorStack 및 Rewriting the Code와의 파트너십우리 대학 모집 팀은 ColorStack 및 Rewriting the Code 와 협력하여 더 많은 여성과 흑인 및 라틴계 학생들이 기술 분야에서 경력을 쌓을 수 있도록 돕습니다.자세히 읽기보고서Women in Tech Mentorship 프로그램Databricks에서는 역사적으로 소외받은 커뮤니티에 대한 직업적 서장과 발전 기회를 제공하는 것이 중요하다고 생각합니다.자세히 읽기보고서2021 InHerSight Award여성들이 평가하는, 여성을 위한 최고의 컴퓨터 소프트웨어 직장에 선정된 것을 기쁘게 생각합니다. 자세한 내용은 InHerSight 프로필에서 확인해 주세요.자세히 읽기데이터의 미래를 함께 만들어 보세요저희는 데이터와 AI를 단순화하고 민주화하는 것을 목표로 하며, 이 미션에는 여러분이 꼭 필요합니다.기회 알아보기제품플랫폼 개요가격오픈 소스 기술Databricks 이용해 보기데모제품플랫폼 개요가격오픈 소스 기술Databricks 이용해 보기데모학습 및 지원관련 문서용어집교육 및 인증헬프 센터법적 고지온라인 커뮤니티학습 및 지원관련 문서용어집교육 및 인증헬프 센터법적 고지온라인 커뮤니티솔루션산업 기준프로페셔널 서비스솔루션산업 기준프로페셔널 서비스회사Databricks 소개Databricks 채용다양성 및 포용성회사 블로그문의처회사Databricks 소개Databricks 채용다양성 및 포용성회사 블로그문의처Databricks 채용 확인하기WorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark 및 Spark 로고는 Apache Software Foundation의 상표입니다.개인 정보 보호 고지|이용약관|귀하의 개인 정보 선택|귀하의 캘리포니아 프라이버시 권리
https://www.databricks.com/dataaisummit/speaker/matthew-doxey/#
Matthew Doxey - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingMatthew DoxeySenior Epidemiologist at Washington State Department of HealthBack to speakersMatt Doxey is a public health data scientist with the Washington State Department of Health's Center for Data Science, where he leads data science and disease modeling initiatives. An epidemiologist by training with experience in global health initiatives and improving health outcomes in under-served communities, Matt is passionate about using innovative, data-driven methods and technologies to protect and enhance community and population health through better data.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/maria-vechtomova
Maria Vechtomova - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingMaria VechtomovaML Engineer at Ahold DelhaizeBack to speakersMaria is a Senior Machine Learning Engineer at Ahold Delhaize. Maria is bridging the gap between data scientists infra and IT teams at different brands and focuses on standardization of machine learning operations across all the brands within Ahold Delhaize. During more than nine years in Data and Analytics, Maria tried herself in different roles, from data scientist to a machine learning engineer, was part of teams in various domains, and have built broad knowledge. Maria believes that a model only starts living when it is in production. For this reason, last seven years, her focus was on the automation and standardization of processes related to machine learning.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/ashwin-gangadhar
Ashwin Gangadhar - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingAshwin GangadharSenior Solutions Architect at MongoDBBack to speakersAshwin is a Senior Solutions architect with MongoDB based in Bangalore India. He has 7+ years of experience in building data driven applications, involving complex architectural and scalability requirements in diverse industries. He has worked extensively in providing solutions for Search relevancy building and delivering solutions in ML / NLP modeling, data processing/data mining pipelines.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/spark/comparing-databricks-to-apache-spark
Comparing Databricks to Apache Spark | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWComparing Apache Spark™ and DatabricksApache Spark capabilities provide speed, ease of use and breadth of use benefits and include APIs supporting a range of use cases:Data integration and ETLInteractive analyticsMachine learning and advanced analyticsReal-time data processingDatabricks builds on top of Spark and adds:Highly reliable and performant data pipelinesProductive data science at scaleWant to learn more? Visit our platform page.Feature ComparisonExpand all | Collapse all column1column2column3 Databricks RuntimeBuilt on Apache Spark and optimized for performance Learn moreNOYESManaged Delta LakeReliable and Performant Data LakesNO YES Integrated WorkspaceInteractive Data Science and CollaborationNO YES Production Jobs And WorkflowsData Pipelines and Workflow AutomationNO YES Enterprise SecurityEnd-to-End Data Security and Compliance Learn moreNO YES IntegrationsCompatible with Common Tools in the EcosystemNO YES Expert SupportUnparalled Support by the Leading Committers of Apache SparkNO YES Additional ResourcesBlogBenchmarking Big Data SQL Platforms in the CloudCustomer StoryHow Hotels.com increased data analyzed by 20x without performance issuesDemoManaged Delta Lake: The best of data lakes, warehouses, and streaming systems.Ready to get started?Try Databricks for freeLearn more about our productTalk to an expertProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/dataaisummit/speaker/adi-polak/#
Adi Polak - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingAdi PolakData & AI Technologist at TechnologistBack to speakersAdi is a world-leading specialist in the field of Data & AI. In her role as Vice President of DevEx at Treeverse, Adi contributes to lakeFS, Git for data project loved by Data & AI practitioners. In her work, she brings her vast industry research and engineering experience to bear in educating and helping teams design, architect, and build cost-effective data systems and machine learning pipelines that emphasize scalability, expertise, and business goals. Adi is a proud Databricks beacon, frequent worldwide presenter and the author of O’Reilly’s book, “Scaling Machine Learning with Spark.”Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/sanjeev-kumar/#
Sanjeev Kumar - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingSanjeev KumarVice President, Data Analytics and AI at Gainwell TechnologiesBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/p/webinar/deep-dive-into-lakehouse-with-delta-lake-complimentary-training
Lakehouse: Take a deep dive | DatabricksFree TrainingLakehouse: Take a deep diveWhat to know about Delta LakeGet an overview of data architecture concepts, an introduction to the Lakehouse paradigm and an in-depth look at Delta Lake features and functionality. You’ll learn about applying software engineering principles with Databricks and discover how to serve data to end users through aggregate tables and Databricks SQL.Find out how to:Use Delta Lake to support your Lakehouse architectureBuild an end-to-end batch and streaming OLAP data pipeline using Delta LakeMake data available for consumption by downstream stakeholdersFollow Databricks best practices as you engineer a Delta design patternWe recommend this course to anyone who’s familiar with data engineering concepts and has a basic knowledge of Delta Lake core features and use cases.Free On-Demand TrainingProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/dataaisummit/speaker/pulkit-chadha
Pulkit Chadha - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingPulkit ChadhaSr. Solutions Architect at DatabricksBack to speakersPulkit Chadha is a Sr. Solutions Architect at Databricks. He has over 12 years of experience working in Data Engineering with expertise in building and optimizing data pipelines using various tools and technologies. Pulkit has worked with enterprises in various industries like Healthcare, Media and Entertainment, Hi-Tech, and Manufacturing providing data engineering solutions to meet enterprises' unique business needs. His work history includes the likes of Dell Services, Adobe, and Databricks.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/blog/2021/12/06/deploying-dbt-on-databricks-just-got-even-simpler.html
Deploying dbt on Databricks Just Got Even Simpler - The Databricks BlogSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCategoriesAll blog postsCompanyCultureCustomersEventsNewsPlatformAnnouncementsPartnersProductSolutionsSecurity and TrustEngineeringData Science and MLOpen SourceSolutions AcceleratorsData EngineeringTutorialsData StreamingData WarehousingData StrategyBest PracticesData LeaderInsightsIndustriesFinancial ServicesHealth and Life SciencesMedia and EntertainmentRetailManufacturingPublic SectorDeploying dbt on Databricks Just Got Even Simplerby Dave Eyler, Takuya Ueshin, Bilal Aslam and Shant HovsepianDecember 6, 2021 in Platform BlogShare this postAt Databricks, nothing makes us happier than making our users more productive, which is why we are delighted to announce a native adapter for dbt. It’s now easier than ever to develop robust data pipelines on Databricks using SQL.dbt is a popular open source tool that lets a new breed of ‘analytics engineer’ build data pipelines using simple SQL. Everything is organized within directories, as plain text, making version control, deployment, and testability simple.With the new dedicated dbt-databricks adapter available in public preview today, dbt developers can get started by simply running pip install dbt-databricks. This package is open source, and built on the brilliant work led by dbt Labs and the other contributors who made dbt-spark possible. Not only did we streamline the installation by removing any dependency on ODBC drivers, we embraced dbt’s “convention over configuration” for maximum performance:dbt models use the Delta format by defaultIncremental models always leverage Delta Lake’s MERGE statementExpensive queries like unique key generation are now accelerated with PhotonMore improvements to this adapter are coming as we continue to improve the overall integration between dbt and the Databricks Lakehouse Platform. With record-breaking performance and full support for standard SQL, it is the best place to run data warehousing workloads, including data pipelines built with dbt.We are also excited about the upcoming addition of dbt Cloud to Partner Connect, Databricks’ one-stop shop for its customers to discover and integrate the best data and AI tools on the market. dbt Cloud is a hosted service made by dbt Labs, which helps data analysts and data engineers collaboratively build and productionize dbt projects. Coming in January, any Databricks customer will be able to start a free trial of dbt Cloud from Partner Connect and automatically integrate the two products. That said, the two products already work great together, and we encourage you to connect dbt Cloud to Databricks today.Speaking of dbt Labs, we hope to see you at their conference, Coalesce, which begins today! Reynold Xin will be having a fireside chat with Drew Banin, CPO for dbt Labs and Ricardo Portillo will be speaking about building data pipelines for Financial Services leveraging dbt and Databricks. You should definitely check it out and join the conversation on the dbt Community Slack in #coalesce-databricks. We look forward to your feedback!Stay tuned for more exciting updates on how Databricks works with dbt and watch our Github repository for new releases.Try Databricks for freeGet StartedRelated postsDeploying dbt on Databricks Just Got Even SimplerDecember 6, 2021 by Dave Eyler, Takuya Ueshin, Bilal Aslam and Shant Hovsepian in Platform Blog At Databricks, nothing makes us happier than making our users more productive, which is why we are delighted to announce a native adapter... See all Platform Blog postsProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/br/product/azure?itm_data=menu-item-azureProduct
Azure Databricks - Reúna todas as suas análises e workloads de IA | DatabricksSkip to main contentPlataformaDatabricks Lakehouse PlatformDelta LakeGovernança de dadosData EngineeringStreaming de dadosArmazenamento de dadosData SharingMachine LearningData SciencePreçosMarketplaceTecnologia de código abertoCentro de segurança e confiançaWEBINAR Maio 18 / 8 AM PT Adeus, Data Warehouse. Olá, Lakehouse. Participe para entender como um data lakehouse se encaixa em sua pilha de dados moderna. Inscreva-se agoraSoluçõesSoluções por setorServiços financeirosSaúde e ciências da vidaProdução industrialComunicações, mídia e entretenimentoSetor públicoVarejoVer todos os setoresSoluções por caso de usoAceleradores de soluçãoServiços profissionaisNegócios nativos digitaisMigração da plataforma de dados9 de maio | 8h PT   Descubra a Lakehouse para Manufatura Saiba como a Corning está tomando decisões críticas que minimizam as inspeções manuais, reduzem os custos de envio e aumentam a satisfação do cliente.Inscreva-se hojeAprenderDocumentaçãoTreinamento e certificaçãoDemosRecursosComunidade onlineAliança com universidadesEventosData+AI SummitBlogLaboratóriosBeaconsA maior conferência de dados, análises e IA do mundo retorna a São Francisco, de 26 a 29 de junho. ParticipeClientesParceirosParceiros de nuvemAWSAzureGoogle CloudConexão de parceirosParceiros de tecnologia e dadosPrograma de parceiros de tecnologiaPrograma de parceiros de dadosBuilt on Databricks Partner ProgramParceiros de consultoria e ISPrograma de parceiros de C&ISSoluções para parceirosConecte-se com apenas alguns cliques a soluções de parceiros validadas.Saiba maisEmpresaCarreiras em DatabricksNossa equipeConselho de AdministraçãoBlog da empresaImprensaDatabricks VenturesPrêmios e reconhecimentoEntre em contatoVeja por que o Gartner nomeou a Databricks como líder pelo segundo ano consecutivoObtenha o relatórioExperimente DatabricksAssista às DemosEntre em contatoInício de sessãoJUNE 26-29REGISTER NOWAzure DatabricksDados e serviço de IA da Databricks disponíveis por meio do Microsoft Azure para armazenar todos os seus dados em um lakehouse aberto e simples e consolidar todas as suas análises e workloads de IA.IntroduçãoAgendar uma demonstraçãoO Azure Databricks é otimizado para o Azure e totalmente integrado ao Azure Data Lake Storage, Azure Data Factory, Azure Synapse Analytics, Power BI e outros serviços do Azure para armazenar todos os seus dados em um lakehouse único e aberto, reunindo todas as suas análises e workloads de IA.Simples Reúna seus dados, análises e IA em uma plataforma comum para todos os casos de uso de dadosAberta Unifique seu ecossistema de dados com código aberto, padrões e formatosColaborativo Unifique suas equipes de dados para colaborar em todos os workflows de dados e IAPor que o Azure Databricks?Workloads do Apache Spark™ com desempenho 50 vezes melhor Implante clusters de compute que escalam automaticamente com o Spark altamente otimizado e que são executados até 50 vezes mais rápido.Saiba maisMilhões de horas de servidor a cada dia O Azure Databricks é usado por milhares de clientes que executam milhões de horas de servidor todos os dias em mais de 34 regiões do Azure.Saiba maisFacilidade de uso Comece com um único clique no Azure Portal, integre nativamente os serviços de dados e os serviços de segurança do Azure e aumente sua produtividade em 25% com data engineering e data science colaborativas.Saiba maisCasos de uso de cada setorServiços financeirosSwiss Re Análise de dados unificada para data engineering, data science e analistas.HSBC Criou uma plataforma de pagamento digital usando Azure Databricks.ABN AMROMelhorou os workflows de análise ao facilitar a colaboração e a produção de insights de IA enquanto aproveita os recursos avançados e automatizados de machine learning.Saiba maisVarejoSaúde e ciências da vidaParticipe de um evento do Azure DatabricksDatabricks, Microsoft e nossos parceiros têm o prazer de organizar esses eventos dedicados ao Azure Databricks. Sinta-se à vontade para nos visitar em um evento perto de você para saber mais sobre o serviço de dados e IA que mais cresce no Azure. A agenda e o formato variam de tempos em tempos. Portanto, verifique a página específica do evento para obter mais detalhes. Saiba maisOtimizado para AzureIntegração perfeita com serviços e armazenamentos de dados do Azure por meio de conectores especializados para acesso rápido a dados e gerenciamento simplificado em seu ambiente. Isso facilita a configuração de controles de segurança, gerenciamento de ambientes e processamento de todos os seus dados do Azure.Saiba maisIntegrações em destaqueAzure Active DirectoryO Single Sign On com o Azure Active Directory é a melhor maneira de se conectar ao Azure Databricks. O Azure Databricks também é compatível com o provisionamento automatizado de usuários com o Azure AD para criar novos usuários, fornecer a eles o nível certo de acesso e remover usuários para desprovisionar o acesso.Azure Data Lake StorageAzure Data FactoryAzure Synapse AnalyticsPower BIAzure DevOpsAzure Virtual NetworkAzure Event HubsAzure Key VaultRecursosArtigos técnicosAzure Databricks para data engineeringAzure Databricks: a melhor plataforma para executar ML e IATrês casos de uso no Azure DatabricksIntrodução ao Apache Spark no Azure DatabricksVídeosAli Ghodsi e Rohan Kumar (CVP, Microsoft) em uma conversa no Keynote Data + AI Summit NA 2021Integração demo com a nuvem do Azure DatabricksIngestão de dados no Delta Lake no Azure DatabricksExplore seus dados usando o Databricks SQLe-booksQuadrante Mágico da Gartner de 2021 para Plataformas de Data Science e Machine LearningModern Cloud Data Platform for DummiesOito etapas para um desenvolvedor aprender Apache Spark™ com Delta LakeApache Spark™ Under the Hood: Introdução à arquitetura central e conceitos básicosTudo pronto para começar?IntroduçãoAgendar uma demonstraçãoProdutoVisão geral da plataformaPreçosTecnologia de código abertoExperimente DatabricksDemoProdutoVisão geral da plataformaPreçosTecnologia de código abertoExperimente DatabricksDemoAprendizagem e suporteDocumentaçãoGlossárioTreinamento e certificaçãoCentral de ajudaInformações legaisComunidade onlineAprendizagem e suporteDocumentaçãoGlossárioTreinamento e certificaçãoCentral de ajudaInformações legaisComunidade onlineSoluçõesPor setorServiços profissionaisSoluçõesPor setorServiços profissionaisEmpresaQuem somosCarreiras em DatabricksDiversidade e inclusãoBlog da empresaEntre em contatoEmpresaQuem somosCarreiras em DatabricksDiversidade e inclusãoBlog da empresaEntre em contatoSee Careers at DatabricksMundialEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Aviso de privacidade|Termos de Uso|Suas opções de privacidade|Seus direitos de privacidade na Califórnia
https://www.databricks.com/dataaisummit/speaker/pascal-van-bellen
Pascal van Bellen - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingPascal van BellenSenior Consultant at ORAYLIS GmbHBack to speakersPascal van Bellen has been working in BI & BigData for more than 7 years. Having already worked in this area for several years, he is involved in building solutions with Azure Databricks. Pascal is a Senior Consultant at Oraylis GmbH in Germany and is responsible for implementation of large scale modern data platforms in various Azure cloud scenarios. As a certified Databricks champion he is particularly interested in the development of large scale BigData use cases using Databricks and Spark.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/krishti-bikal/#
Krishti Bikal - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingKrishti BikalSenior Executive - Director BI & Analytics at EmeraldBack to speakersKrishti is a Senior technical Executive at EmeraldX, where he is currently leading various Data Analytics projects. One of them is implementation of ThoughtSpot Everywhere for Emerald's Customer Hub. He has over 15 years of industry experience in Data Engineering and Analytics. He holds B. Tech in Mechanical Engineering from NIT Patna, India and Masters in MIS from University of Illinois, USA.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/darth-vader/#
Darth Vader - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingDarth VaderSith Lord at The Galactic EmpireBack to speakersOnce the heroic Jedi Knight named Anakin Skywalker, Darth Vader was seduced by the dark side of the Force. Forever scarred by his defeat on Mustafar, Vader was transformed into a cybernetically-enhanced Sith Lord. At the dawn of the Empire, Vader led the Empire’s eradication of the Jedi Order and the search for survivors. He remained in service of the Emperor -- the evil Darth Sidious -- for decades, enforcing his Master’s will and seeking to crush the Rebel Alliance and other detractors.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/leo-duncan
Leo Duncan - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingLeo DuncanSr. Solutions Architect at Kyvos Insights Inc.Back to speakersLeo Duncan is a Senior Solutions Architect at Kyvos Insights Inc. With over 18 years of analytics experience, he has designed and developed game-changing BI solutions for some of the world's most recognized brands. Leo's innovative approach and strategic vision have earned him a reputation as an analytics genius, empowering organizations to make informed, data-driven decisions.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/behzad-bordbar
Behzad Bordbar - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingBehzad BordbarLead Data Scientist at Marks & SpencerBack to speakersBehzad started working at M&S in Jan 2021 as Lead Data Scientist. As part of the Data Science Retails team, he is involved in the digital transformation of Retail operations utilizing Machine Learning and Artificial Intelligence. He has a PhD in mathematics and over 20 years of experience solving challenging and complex business problems using innovative data solutions. He has published over 120 technical and scientific papers and contributed to several open-source projects.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/paul-roome/#
Paul Roome - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingPaul RoomeStaff Product Manager at DatabricksBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/kr/try-databricks?itm_data=NavBar-TryDatabricks-Trial
Databricks 무료로 체험하기 | DatabricksDatabricks 무료로 체험하기AWS, Microsoft Azure 또는 Google Cloud 중 원하는 클라우드 서비스에서 14일 동안 Databricks 플랫폼 전체를 무료로 체험해 보세요.데이터 수집 단순화 및 ETL 자동화가 가능합니다.수백 개의 소스에서 데이터를 수집해 보세요. 간단한 방식으로 데이터 파이프라인을 구축하실 수 있습니다.원하는 언어로 협업이 가능합니다.공동 저작, 자동 버전 관리, Git 통합, RBAC를 활용하여 Python, R, Scala 및 SQL로 코딩하세요.클라우드 데이터 웨어하우스보다 12배 더 우수한 가격 대비 성능전 세계 7,000개 고객사가 BI부터 AI에 이르는 모든 워크로드를 위해서 Databricks 를 선택한 이유를 알아보세요.Databricks 계정 생성하기1/2이름성회사 이메일회사직함전화번호(선택사항)보기 중에서 선택하세요국가계속개인 정보 보호 고지(업데이트됨)이용약관귀하의 개인 정보 선택귀하의 캘리포니아 프라이버시 권리
https://www.databricks.com/resources/webinar/innovate-speed-data?itm_data=home-navmenu-solution-innovatespeeddata
Innovate at the speed of data | DatabricksOn DemandInnovate at the speed of dataExplore the Databricks Lakehouse for ManufacturingAvailable on demandLegacy tools and data silos hinder the analytics that help you optimize your supply chain. They also prevent the single view of the customer that drives innovation. That’s why many manufacturers are migrating to the Databricks Lakehouse for Manufacturing. With this platform, they’re delivering precise service outcomes in the field, optimizing supply chain operations, boosting productivity and innovating at the speed of data.Watch to learn more about the lakehouse and how Corning is using the lakehouse to make critical decisions that minimize manual inspections, lower shipping costs and increase customer satisfaction.You’ll learn how the lakehouse can help you achieve:90% lower cost for new manufacturing lines50x faster time to insight5%-10% reduction in unplanned downtime and costStreaming IoT data from millions of assetsSpeakersDenis KamotskyPrincipal Software EngineerCorningShiv TrisalGlobal Industry Director - Manufacturing & LogisticsDatabricksSam SteinyPrincipal Industry Marketing Manager - Retail & ManufacturingDatabricksJoin usProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/blog/2020/10/20/detecting-at-risk-patients-with-real-world-data.html
How to Detect At-risk Patients with Real World Data - The Databricks BlogSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCategoriesAll blog postsCompanyCultureCustomersEventsNewsPlatformAnnouncementsPartnersProductSolutionsSecurity and TrustEngineeringData Science and MLOpen SourceSolutions AcceleratorsData EngineeringTutorialsData StreamingData WarehousingData StrategyBest PracticesData LeaderInsightsIndustriesFinancial ServicesHealth and Life SciencesMedia and EntertainmentRetailManufacturingPublic SectorDetecting At-risk Patients with Real World DataHow to use the Machine Learning Runtime and MLflow on top of a health Delta Lake to predict patient diseaseby Amir Kermany and Frank Austin NothaftOctober 20, 2020 in Engineering BlogShare this postWith the rise of low cost genome sequencing and AI-enabled medical imaging, there has been substantial interest in precision medicine. In precision medicine, we aim to use data and AI to come up with the best treatment for a disease. While precision medicine has improved outcomes for patients diagnosed with rare diseases and cancers, precision medicine is reactive: the patient has to be sick for precision medicine to be deployed.When we look at healthcare spending and outcomes, there is a tremendous opportunity to improve cost-of-care and quality of living by preventing chronic conditions such as diabetes, heart disease, or substance use disorders. In the United States, 7 out of 10 deaths and 85% of healthcare spending is driven by chronic conditions, and similar trends are found in Europe and Southeast Asia. Noncommunicable diseases are generally preventable through patient education and by addressing underlying issues that drive the chronic condition. These issues can include underlying biological risk factors such as known genetic risks that drive neurological conditions, socioeconomic factors like environmental pollution or lack of access to healthy food/preventative care, and behavioral risks such as smoking status, alcohol consumption, or having a sedentary lifestyle.Precision prevention is focused on using data to identify patient populations at risk of developing a disease, and then providing interventions that reduce disease risk. An intervention might include a digital app that remotely monitors at-risk patients and provides lifestyle and treatment recommendations, increased monitoring of disease status, or offering supplemental preventative care. However, deploying these interventions first depends on identifying the patients at risk.One of the most powerful tools for identifying patients at risk is the use of real world data (RWD), a term that collectively refers to data generated by the healthcare ecosystem, such as electronic medical records (EMR) and health records (EHR) from hospitalizations, clinical practices, pharmacies, healthcare providers, and increasingly data collected from other sources such as genomics, social media, and wearables. In our last blog we demonstrated how to build a clinical data lake from EHR data. In this blog, we build on that by using the Databricks Unified Data Analytics Platform to track a patient’s journey and create a machine learning model. Using this model, given a patient’s encounter history and demographics information, we can assess the risk of a patient for a given condition within a given window of time. In this example, we will look at drug overuse, an important topic given the broad range of poor health outcomes driven by substance use disorders. By tracking our models using MLflow, we make it easy to track how models have changed over time, adding confidence to the process of deploying a model into patient care.Disease prediction using machine learning on DatabricksData preparationTo train a model to predict risk at a given time, we need a dataset that captures relevant demographic information about the patient (such as age at time of encounter, ethnicity etc) as well as time series data about the patient’s diagnostic history. We can then use this data to train a model that learns the diagnoses and demographic risks that influence the patient’s likelihood of being diagnosed with a disease in the upcoming time period. Figure 1: Data schemas and relationships between tables extracted from the EHRTo train this model, we can leverage the patient's encounter records and demographic information, as would be available in an electronic health record (EHR). Figure 1 depicts the tables we will use in our workflow. These tables were prepared using the notebooks from our previous blog. We will proceed to load encounters, organizations and patient data (with obfuscated PII information) from Delta Lake and create a dataframe of all patient encounters along with patient demographic information. patient_encounters = ( encounters .join(patients, ['PATIENT']) .join(organizations, ['ORGANIZATION']) ) display(patient_encounters.filter('REASONDESCRIPTION IS NOT NULL').limit(10)) Based on the target condition, we also select a set of patients that qualify to be included in the training data. Namely, we include cases, patients that have been diagnosed with the disease at least once through their encounter history, and an equal number of controls, patients without any history of the disease. positive_patients = ( patient_encounters .select('PATIENT') .where(lower("REASONDESCRIPTION").like("%{}%".format(condition))) .dropDuplicates() .withColumn('is_positive',lit(True)) ) negative_patients = ( all_patients .join(positive_patients,on=['PATIENT'],how='left_anti') .limit(positive_patients.count()) .withColumn('is_positive',lit(False)) ) patients_to_study = positive_patients.union(negative_patients) Now we limit our set of encounters to patients included in the study. qualified_patient_encounters_df = ( patient_encounters .join(patients_to_study,on=['PATIENT']) .filter("DESCRIPTION is not NUll") ) Now that we have the records of interest, our next step is to add features. For this forecasting task, in addition to demographic information, we choose the total number of times having been diagnosed with the condition or any known coexisting conditions (comorbidities) and the number of previous encounters as historical context for a given encounter.Although for most diseases there is extensive literature on comorbid conditions, we can also leverage the data in our real world dataset to identify comorbidities associated with the target condition. comorbid_conditions = ( positive_patients.join(patient_encounters, ['PATIENT']) .where(col('REASONDESCRIPTION').isNotNull()) .dropDuplicates(['PATIENT', 'REASONDESCRIPTION']) .groupBy('REASONDESCRIPTION').count() .orderBy('count', ascending=False) .limit(num_conditions) ) In our code, we use notebook widgets to specify the number of comorbidities to include, as well as the length of time (in days) to look across encounters. These parameters are logged using MLflow’s tracking API.Now we need to add comorbidity features to each encounter. Corresponding to each comorbidity we add a column that indicates how many times the condition of interest has been observed in the past, i.e.whereWe add these features in two steps. First, we define a function that adds comorbidity indicator functions (i.e. xi,c): def add_comorbidities(qualified_patient_encounters_df,comorbidity_list): output_df = qualified_patient_encounters_df idx = 0 for comorbidity in comorbidity_list: output_df = ( output_df .withColumn("comorbidity_%d" % idx, (output_df['REASONDESCRIPTION'].like('%' + comorbidity['REASONDESCRIPTION'] + '%')).cast('int')) .withColumn("comorbidity_%d" % idx,coalesce(col("comorbidity_%d" % idx),lit(0))) # replacing null values with 0 .cache() ) idx += 1 return(output_df) And then we sum these indicator functions over a contiguous range of days using Spark SQL’s powerful support for window functions: def add_recent_encounters(encounter_features): lowest_date = ( encounter_features .select('START_TIME') .orderBy('START_TIME') .limit(1) .withColumnRenamed('START_TIME', 'EARLIEST_TIME') ) output_df = ( encounter_features .crossJoin(lowest_date) .withColumn("day", datediff(col('START_TIME'), col('EARLIEST_TIME'))) .withColumn("patient_age", datediff(col('START_TIME'), col('BIRTHDATE'))) ) w = ( Window.orderBy(output_df['day']) .partitionBy(output_df['PATIENT']) .rangeBetween(-int(num_days), -1) ) for comorbidity_idx in range(num_conditions): col_name = "recent_%d" % comorbidity_idx output_df = ( output_df .withColumn(col_name, sum(col("comorbidity_%d" % comorbidity_idx)).over(w)) .withColumn(col_name,coalesce(col(col_name),lit(0))) ) return(output_df) After adding comorbidity features, we need to add the target variable, which indicates whether the patient is diagnosed with the target condition in a given window of time in the future (for example a month after the current encounter). The logic of this operation is very similar to the previous step, with the difference that the window of time covers future events. We only use a binary label, indicating whether the diagnosis we are interested in will occur in the future or not. def add_label(encounter_features,num_days_future): w = ( Window.orderBy(encounter_features['day']) .partitionBy(encounter_features['PATIENT']) .rangeBetween(0,num_days_future) ) output_df = ( encounter_features .withColumn('label', max(col("comorbidity_0")).over(w)) .withColumn('label',coalesce(col('label'),lit(0))) ) return(output_df) Now we write these features into a feature store within Delta Lake. To ensure reproducibility, we add the mlflow experiment ID and the run ID as a column to the feature store. The advantage of this approach is that we receive more data, we can add new features to the featurestore that can be re-used to refer to in the future.Controlling for quality issues in our dataBefore we move ahead with the training task, we take a look at the data to see how different labels are distributed among classes. In many applications of binary classification, one class can be rare, for example in disease prediction. This class imbalance will have a negative impact on the learning process. During the estimation process, the model tends to focus on the majority class at the expense of rare events. Moreover, the evaluation process is also compromised. For example, in an imbalance dataset with 0/1 labels distributed as 95% and %5 respectively,  a model that always predicts 0, would have an accuracy of 95%. If the labels are imbalanced, then we need to apply one of the common techniques for correcting for imbalanced data.Looking at our training data, we see (Figure 2) that this is a very imbalanced dataset: over 95% of the observed time windows do not show evidence of a diagnosis. To adjust for imbalance, we can either downsample the control class or generate synthetic samples. This choice depends on the dataset size and the number of features. In this example, we downsample the majority class to obtain a balanced dataset. Note that in practice, you can choose a combination of methods, for example downsample the majority class and also assign class weights in your training algorithm. df1 = dataset_df.filter('label==1') n_df1=df1.count() df2 = dataset_df.filter('label==0').sample(False,0.9).limit(n_df1) training_dataset_df = df1.union(df2).sample(False,1.0) display(training_dataset_df.groupBy('label').count()) Model trainingTo train the model, we augment our conditions with a subset of demographic and comorbidity features, apply labels to each observation, and pass this data to a model for training downstream. For example, here we augment our recent diagnosed comorbidities with Encounter Class (e.g., was this appointment for preventative care or was it an ER visit?, ), and the cost of the visits, and for demographic information, we choose Race, Gender, Zip and the patient's age at the time of the encounter.Most often, although the original clinical data can add up to terabytes, after performing filtering and limiting records based on inclusion/exclusion criteria, we end up with a dataset that can be trained on a single machine. We can easily transform spark dataframes to pandas dataframes and train a model based on any algorithm of choice. When using the Databricks ML runtime, we have access to a wide range of open ML libraries readily available.Any machine learning algorithm takes a set of parameters (hyper parameters), and depending on the input parameters the score can change. In addition, in some cases wrong parameters or algorithms can result in overfitting. To ensure that the model performs well,  we use hyperparameter tuning to choose the best model architecture and then we will train the final model by specifying the parameters that were obtained from this step.To perform model tuning, we first need to pre-process the data. In this dataset, in addition to numeric features (counts of recent comorbidities for example), we also have the categorical demographic data that we would like to use. For categorical data, the best approach is to use one-hot-encoding. There are two main reasons for this: first, most classifiers (logistic regression in this case), operate on numeric features. Second, if we simply convert categorical variables to numeric indices, it would introduce ordinality in our data which can mislead the classifier: for example, if we convert states names to indices, e.g. California to 5 and New York to 23, then New York becomes “bigger” than California. While this reflects the index of each state name in an alphabetized list, in the context of our model, this ordering does not mean anything. One-hot-encoding eliminates this effect.The pre-processing step in this case does not take any input parameters and  hyperparameters only affect the classifier and not the preprocessing part. Hence, we separately perform pre-processing and then use the resulting dataset for model tuning: from sklearn.preprocessing import OneHotEncoder import numpy as np def pre_process(training_dataset_pdf): X_pdf=training_dataset_pdf.drop('label',axis=1) y_pdf=training_dataset_pdf['label'] onehotencoder = OneHotEncoder(handle_unknown='ignore') one_hot_model = onehotencoder.fit(X_pdf.values) X=one_hot_model.transform(X_pdf) y=y_pdf.values return(X,y) Next, we would like to choose the best parameters to the model. For this classification, we use LogisticRegression with elastic net penalization. Note that after applying one-hot-encoding, depending on the cardinality of the categorical variable in question, we can end up with many features which can surpass the number of samples. To avoid overfitting for such problems, a penalty is applied to the objective function. The advanaget of elastic net regularization is that it combines two penalization techniques (LASSO and Ridge Regression) and the degree of the mixture can be controlled by a single variable, during hyperparameter tuning.To improve on the model, we search a grid of hyperparameters using hyperopt to find the best parameters. In addition, we use the SparkTrials mode of hyperopt to perform the hyperparameter search in parallel.This process leverages Databricks’ managed MLflow to automatically log parameters and metrics corresponding to each hyperparameter run. To validate each set of parameters, we use a k-fold cross validation skim using F1 score as the metric to assess the model. Note that since k-fold cross validation generates multiple values, we choose the minimum of the scores (the worst case scenario) and try to maximize that when we use hyperopt. from math import exp def params_to_lr(params): return { 'penalty': 'elasticnet', 'multi_class': 'ovr', 'random_state': 43, 'n_jobs': -1, 'solver': 'saga', 'tol': exp(params['tol']), # exp() here because hyperparams are in log space 'C': exp(params['C']), 'l1_ratio': exp(params['l1_ratio']) } def tune_model(params): with mlflow.start_run(run_name='tunning-logistic-regression',nested=True) as run: clf = LogisticRegression(**params_to_lr(params)).fit(X, y) loss = - cross_val_score(clf, X, y,n_jobs=-1, scoring='f1').min() return {'status': STATUS_OK, 'loss': loss} To improve our search over the space, we choose the grid of parameters in logspace and define a transformation function to convert the suggested parameters by hyperopt. For a great overview of the approach and why we chose to define the hyperparameter space like this, look at this talk that covers how you can manage the end-to-end ML life cycle on Databricks. from hyperopt import fmin, hp, tpe, SparkTrials, STATUS_OK search_space = { # use uniform over loguniform here simply to make metrics show up better in mlflow comparison, in logspace 'tol': hp.uniform('tol', -3, 0), 'C': hp.uniform('C', -2, 0), 'l1_ratio': hp.uniform('l1_ratio', -3, -1), } spark_trials = SparkTrials(parallelism=2) best_params = fmin(fn=tune_model, space=search_space, algo=tpe.suggest, max_evals=32, rstate=np.random.RandomState(43), trials=spark_trials) The outcome of this run is the best parameters, assessed based on the F1-score from our cross validation. params_to_lr(best_params) Out[46]: {'penalty': 'elasticnet', 'multi_class': 'ovr', 'random_state': 43, 'n_jobs': -1, 'solver': 'saga', 'tol': 0.06555920596441883, 'C': 0.17868321158011416, 'l1_ratio': 0.27598949120226646} Now let’s take a look at the MLflow dashboard. MLflow automatically groups all runs of the hyperopt together and we can use a variety of plots to inspect the impact of each hyperparameter on the loss function, as shown in Figure 3. This is particularly important for getting a better understanding of the behavior of our model and the effect of the hyperparameters. For example, we noted that lower values for C, the inverse of regularization strength, result in higher values for F1. Fig 3. Parallel coordinates plots for our models in MLflow.After finding the optimal parameter combinations, we train a binary classifier with the optimal hyperparameters and log the model using MLflow. MLflow’s model api makes it easy to store a model, regardless of the underlying library that was used for training, as a python function that can later be called during model scoring. To help with model discoverability, we log the model with a name associated with the target condition (for example in this case, “drug-overdose”). import mlflow.sklearn import matplotlib.pyplot as plt from sklearn.pipeline import Pipeline from mlflow.models.signature import infer_signature ## since we want the model to output probabilities (risk) rather than predicted labels, we overwrite ## mlflow.pyfun's predict method: class SklearnModelWrapper(mlflow.pyfunc.PythonModel): def __init__(self, model): self.model = model def predict(self, context, model_input): return self.model.predict_proba(model_input)[:,1] def train(params): with mlflow.start_run(run_name='training-logistic-regression',nested=True) as run: mlflow.log_params(params_to_lr(params)) X_arr=training_dataset_pdf.drop('label',axis=1).values y_arr=training_dataset_pdf['label'].values ohe = OneHotEncoder(handle_unknown='ignore') clf = LogisticRegression(**params_to_lr(params)).fit(X, y) pipe = Pipeline([('one-hot', ohe), ('clf', clf)]) lr_model = pipe.fit(X_arr, y_arr) score=cross_val_score(clf, ohe.transform(X_arr), y_arr,n_jobs=-1, scoring='accuracy').mean() wrapped_lr_model = SklearnModelWrapper(lr_model) model_name= '-'.join(condition.split()) mlflow.log_metric('accuracy',score) mlflow.pyfunc.log_model(model_name, python_model=wrapped_lr_model) displayHTML('The model accuracy is: <b style="color: tomato;"> %s </b>'%(score)) return(mlflow.active_run().info) Now, we can train the model by passing the best params obtained from the previous step.Note that for model training, we have included preprocessing (one hot encoding) as part of the sklearn pipeline and log the encoder and classifier as one model. In the next step, we can simply call the model on patient data and assess their risk.Model deployment and productionalizationAfter training the model and logging it to MLflow, the next step is to use the model for scoring new data. One of the features of MLflow is that you can search through experiments based on different tags. For example, in this case we use the run name that was specified during model training to retrieve the artifact URI of the trained models. We can then order the retrieved experiments based on key metrics. import mlflow best_run=mlflow.search_runs(filter_string="tags.mlflow.runName = 'training-logistic-regression'",order_by=['metrics.accuracy DESC']).iloc[0] model_name='drug-overdose' clf=mlflow.pyfunc.load_model(model_uri="%s/%s"%(best_run.artifact_uri,model_name)) clf_udf=mlflow.pyfunc.spark_udf(spark, model_uri="%s/%s"%(best_run.artifact_uri,model_name)) Once we have chosen a specific model, we can then load the model by specifying the model URI and name:We can also use Databricks’s model registry to manage model versions, production lifecycle and also easy model serving.Translating disease prediction into precision preventionIn this blog, we walked through the need for a precision prevention system that identifies clinical and demographic covariates that drive the onset of chronic conditions. We then looked at an end-to-end machine learning workflow that used simulated data from an EHR to identify patients who were at risk of drug overdose. At the end of this workflow, we were able to export the ML model we trained from MLflow, and we applied it to a new stream of patient data.While this model is informative, it doesn’t have impact until translated into practice. In real world practice, we have worked with a number of customers to deploy these and similar systems into production. For instance, at the Medical University of South Carolina, they were able to deploy live-streaming pipelines that processed EHR data to identify patients at risk of sepsis. This led to detection of sepsis-related patient decline 8 hours in advance. In a similar system at INTEGRIS Health, EHR data was monitored for emerging signs of pressure ulcer development. In both settings, whenever a patient was identified, a care team was alerted to their condition. In the health insurance setting, we have worked with Optum to deploy a similar model. They were able to develop a disease prediction engine that used recurrent neural networks in a long-term short-term architecture to identify disease progression with good generalization across nine different disease areas. This model was used to align patients with preventative care pathways, leading to improved outcomes and cost-of-care for chronic disease patients.While most of our blog has focused on the use of disease prediction algorithms in healthcare settings, there is also a strong opportunity to build and deploy these models in a pharmaceutical setting. Disease prediction models can provide insights into how drugs are being used in a postmarket setting, and even detect previously undetected protective effects that can inform label expansion efforts. Additionally, disease prediction models can be useful when looking at clinical trial enrollment for rare—or otherwise underdiagnosed—diseases. By creating a model that looks at patients who were misdiagnosed prior to receiving a rare disease diagnosis, we can create educational material that educates clinicians about common misdiagnosis patterns and hopefully create trial inclusion criteria that leads to increased trial enrollment and higher efficacy.Get started With precision prevention on a health Delta LakeIn this blog, we demonstrated how to use machine learning on real-world data to identify patients at risk of developing a chronic disease. To learn more about using Delta Lake to store and process clinical datasets, download our free eBook on working with real world clinical datasets. You can also start a free trial today using the patient risk scoring notebooks from this blog.Try Databricks for freeGet StartedRelated postsDetecting At-risk Patients with Real World DataOctober 20, 2020 by Amir Kermany and Frank Austin Nothaft in Engineering Blog With the rise of low cost genome sequencing and AI-enabled medical imaging, there has been substantial interest in precision medicine. In precision medicine... 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Mitch Ertle - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingMitch ErtlePartner Solutions Engineer at Sigma ComputingBack to speakersMitch Ertle is an experienced data practitioner with over a decade of experience in Data Analytics. Prior to joining Sigma, Mitch spent three years leading data teams on Databricks.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
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データブリックスのデータレイクハウスプラットフォーム|デモのご依頼を承っております | DatabricksSkip to main contentプラットフォームデータブリックスのレイクハウスプラットフォームDelta Lakeデータガバナンスデータエンジニアリングデータストリーミングデータウェアハウスデータ共有機械学習データサイエンス料金Marketplaceオープンソーステクノロジーセキュリティ&トラストセンターウェビナー 5 月 18 日午前 8 時 PT さようなら、データウェアハウス。こんにちは、レイクハウス。 データレイクハウスが最新のデータスタックにどのように適合するかを理解するために出席してください。 今すぐ登録ソリューション業種別のソリューション金融サービス医療・ライフサイエンス製造通信、メディア・エンターテイメント公共機関小売・消費財全ての業界を見るユースケース別ソリューションソリューションアクセラレータプロフェッショナルサービスデジタルネイティブビジネスデータプラットフォームの移行5月9日 |午前8時(太平洋標準時)   製造業のためのレイクハウスを発見する コーニングが、手作業による検査を最小限に抑え、輸送コストを削減し、顧客満足度を高める重要な意思決定をどのように行っているかをご覧ください。今すぐ登録学習ドキュメントトレーニング・認定デモ関連リソースオンラインコミュニティ大学との連携イベントDATA+AI サミットブログラボBeacons2023年6月26日~29日 直接参加するか、基調講演のライブストリームに参加してくださいご登録導入事例パートナークラウドパートナーAWSAzureGoogle CloudPartner Connect技術・データパートナー技術パートナープログラムデータパートナープログラムBuilt on Databricks Partner ProgramSI コンサルティングパートナーC&SI パートナーパートナーソリューションDatabricks 認定のパートナーソリューションをご利用いただけます。詳しく見る会社情報採用情報経営陣取締役会Databricks ブログニュースルームDatabricks Ventures受賞歴と業界評価ご相談・お問い合わせDatabricks は、ガートナーのマジック・クアドラントで 2 年連続でリーダーに位置付けられています。レポートをダウンロードDatabricks 無料トライアルデモを見るご相談・お問い合わせログインJUNE 26-29REGISTER NOWデータブリックスのレイクハウスプラットフォームデータウェアハウス、AI のユースケースを 1 つのプラットフォームに集約無料トライアル WEBINAR • Goodbye, Data Warehouse. Hello, Lakehouse. Attend on May 18 and get a $100 credit toward a Databricks certification course Register nowシンプル、オープン、マルチクラウドDatabricks の「レイクハウス」プラットフォームは、データウェアハウスとデータレイクの最良の要素の融合体として、データウェアハウスの信頼性、ガバナンス、性能と、データレイクの柔軟性、機械学習との親和性、オープンフォーマットを提供します。従来の方法では分断されていた分析、データサイエンス、機械学習を統合することで、データのサイロ化を解消し、最新のデータスタックをシンプルにします。オープンソース、オープンスタンダードが基盤となっており、最大限の柔軟性を提供します。また、一貫したデータ管理、セキュリティ、ガバナンスが、業務の効率化と革新を支援します。シンプル統合プラットフォームが、シンプルなデータアーキテクチャによってデータサイロをなくし、これまで分断されていた分析、データサイエンス、機械学習を統合します。さらに、レイクハウスによって複雑さとコストを解消することで、分析と AI の取り組みの成果を最大化できます。オープンDelta Lake がレイクハウスのオープンな基盤となっており、データレイクのデータに信頼性と世界記録を更新するパフォーマンスをもたらします。閉鎖された独自の環境を回避し、データの共有を容易にします。また、オープンソースのデータプロジェクトや Databricks のパートナーネットワークのリソースを最大限に活用して、最新のデータスタックを構築できます。データランドスケープ全体の450 +パートナーの詳細をご覧ください詳細情報マルチクラウドDatabricks のレイクハウスプラットフォームは、クラウド間で一貫した管理、セキュリティ、ガバナンスを提供します。データ・AI を扱う現行の取り組みのために既に導入している各クラウド用に、プロセスを再構築する必要はありません。データチームは、あらゆるデータを最大限活用した新たな知見の抽出に注力できます。詳細情報データの可能性を解き放つ — そしてデータチームDatabricks Lakehouse プラットフォームがデータと AI ワークロードを実現する方法の詳細データエンジニアリングDatabricks SQL機械学習関連リソース 関連リソース一覧 データエンジニアリングにおける Databricks 活用のメリットとは?eBook や動画などの関連リソースが見つかります。 詳しく見るレイクハウスデータレイクとデータウェアハウスとは?それぞれの強み・弱みと次世代のデータ管理システム「データレイクハウス」を解説初心者のためのモダンクラウドデータプラットフォームレイクハウスパラダイムの誕生データウェアハウスの父であるビル・インモンによるデータレイクハウスの台頭Delta LakeデータエンジニアリングのビッグブックDelta Lake 解説の決定版eBook:Delta Lake シリーズオンデマンド動画:Delta Lake ― レイクハウスの基盤機械学習eBook:機械学習ライフサイクルの標準化オンラインイベント:機械学習プラットフォームの構築無料お試し・その他ご相談を承りますDatabricks 無料トライアル製品プラットフォーム料金オープンソーステクノロジーDatabricks 無料トライアルデモ製品プラットフォーム料金オープンソーステクノロジーDatabricks 無料トライアルデモ学習・サポートドキュメント用語集トレーニング・認定ヘルプセンター法務オンラインコミュニティ学習・サポートドキュメント用語集トレーニング・認定ヘルプセンター法務オンラインコミュニティソリューション業種別プロフェッショナルサービスソリューション業種別プロフェッショナルサービス会社情報会社概要採用情報ダイバーシティ&インクルージョンDatabricks ブログご相談・お問い合わせ会社情報会社概要採用情報ダイバーシティ&インクルージョンDatabricks ブログご相談・お問い合わせ採用情報言語地域English (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.プライバシー通知|利用規約|プライバシー設定|カリフォルニア州のプライバシー権利
https://www.databricks.com/jp/company/partners/consulting-and-si
コンサルティングパートナー | DatabricksSkip to main contentプラットフォームデータブリックスのレイクハウスプラットフォームDelta Lakeデータガバナンスデータエンジニアリングデータストリーミングデータウェアハウスデータ共有機械学習データサイエンス料金Marketplaceオープンソーステクノロジーセキュリティ&トラストセンターウェビナー 5 月 18 日午前 8 時 PT さようなら、データウェアハウス。こんにちは、レイクハウス。 データレイクハウスが最新のデータスタックにどのように適合するかを理解するために出席してください。 今すぐ登録ソリューション業種別のソリューション金融サービス医療・ライフサイエンス製造通信、メディア・エンターテイメント公共機関小売・消費財全ての業界を見るユースケース別ソリューションソリューションアクセラレータプロフェッショナルサービスデジタルネイティブビジネスデータプラットフォームの移行5月9日 |午前8時(太平洋標準時)   製造業のためのレイクハウスを発見する コーニングが、手作業による検査を最小限に抑え、輸送コストを削減し、顧客満足度を高める重要な意思決定をどのように行っているかをご覧ください。今すぐ登録学習ドキュメントトレーニング・認定デモ関連リソースオンラインコミュニティ大学との連携イベントDATA+AI サミットブログラボBeacons2023年6月26日~29日 直接参加するか、基調講演のライブストリームに参加してくださいご登録導入事例パートナークラウドパートナーAWSAzureGoogle CloudPartner Connect技術・データパートナー技術パートナープログラムデータパートナープログラムBuilt on Databricks Partner ProgramSI コンサルティングパートナーC&SI パートナーパートナーソリューションDatabricks 認定のパートナーソリューションをご利用いただけます。詳しく見る会社情報採用情報経営陣取締役会Databricks ブログニュースルームDatabricks Ventures受賞歴と業界評価ご相談・お問い合わせDatabricks は、ガートナーのマジック・クアドラントで 2 年連続でリーダーに位置付けられています。レポートをダウンロードDatabricks 無料トライアルデモを見るご相談・お問い合わせログインJUNE 26-29REGISTER NOWSI コンサルティングパートナーデータブリックスの SI コンサルティングパートナーは、お客様とグローバルに連携し、データブリックスのレイクハウスプラットフォームの構築、展開、移行をサポートしています。データブリックスの SI パートナーは、それぞれの独自のポジションで、お客様におけるデータエンジニアリング、コラボレーション型データサイエンス、フルライフサイクルの機械学習、ビジネス分析イニシアチブの展開と拡張をデータブリックスと連携して支援しています。 テクノロジー、業界およびユースケースに関する専門知識を活かし、お客様がデータブリックスのレイクハウスプラットフォームを最大限に活用できるようサポートします。ビジネスに合わせたデータ変革戦略の立案、データの近代化と移行、データ管理とガバナンスなど、多岐にわたるサービスを提供します。Loading...製品プラットフォーム料金オープンソーステクノロジーDatabricks 無料トライアルデモ製品プラットフォーム料金オープンソーステクノロジーDatabricks 無料トライアルデモ学習・サポートドキュメント用語集トレーニング・認定ヘルプセンター法務オンラインコミュニティ学習・サポートドキュメント用語集トレーニング・認定ヘルプセンター法務オンラインコミュニティソリューション業種別プロフェッショナルサービスソリューション業種別プロフェッショナルサービス会社情報会社概要採用情報ダイバーシティ&インクルージョンDatabricks ブログご相談・お問い合わせ会社情報会社概要採用情報ダイバーシティ&インクルージョンDatabricks ブログご相談・お問い合わせ採用情報言語地域English (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.プライバシー通知|利用規約|プライバシー設定|カリフォルニア州のプライバシー権利
https://www.databricks.com/br/product/delta-sharing
Delta Sharing | DatabricksSkip to main contentPlataformaDatabricks Lakehouse PlatformDelta LakeGovernança de dadosData EngineeringStreaming de dadosArmazenamento de dadosData SharingMachine LearningData SciencePreçosMarketplaceTecnologia de código abertoCentro de segurança e confiançaWEBINAR Maio 18 / 8 AM PT Adeus, Data Warehouse. Olá, Lakehouse. Participe para entender como um data lakehouse se encaixa em sua pilha de dados moderna. Inscreva-se agoraSoluçõesSoluções por setorServiços financeirosSaúde e ciências da vidaProdução industrialComunicações, mídia e entretenimentoSetor públicoVarejoVer todos os setoresSoluções por caso de usoAceleradores de soluçãoServiços profissionaisNegócios nativos digitaisMigração da plataforma de dados9 de maio | 8h PT   Descubra a Lakehouse para Manufatura Saiba como a Corning está tomando decisões críticas que minimizam as inspeções manuais, reduzem os custos de envio e aumentam a satisfação do cliente.Inscreva-se hojeAprenderDocumentaçãoTreinamento e certificaçãoDemosRecursosComunidade onlineAliança com universidadesEventosData+AI SummitBlogLaboratóriosBeaconsA maior conferência de dados, análises e IA do mundo retorna a São Francisco, de 26 a 29 de junho. ParticipeClientesParceirosParceiros de nuvemAWSAzureGoogle CloudConexão de parceirosParceiros de tecnologia e dadosPrograma de parceiros de tecnologiaPrograma de parceiros de dadosBuilt on Databricks Partner ProgramParceiros de consultoria e ISPrograma de parceiros de C&ISSoluções para parceirosConecte-se com apenas alguns cliques a soluções de parceiros validadas.Saiba maisEmpresaCarreiras em DatabricksNossa equipeConselho de AdministraçãoBlog da empresaImprensaDatabricks VenturesPrêmios e reconhecimentoEntre em contatoVeja por que o Gartner nomeou a Databricks como líder pelo segundo ano consecutivoObtenha o relatórioExperimente DatabricksAssista às DemosEntre em contatoInício de sessãoJUNE 26-29REGISTER NOWDelta SharingUm padrão aberto para compartilhamento seguro de ativos de dadosComeçarAssista à demonstraçãoO Delta Sharing da Databricks é uma solução aberta para compartilhar com segurança dados em tempo real de seu lakehouse para qualquer plataforma de computação.Principais benefíciosCompartilhamento aberto entre plataformasEvite amarras de fornecedores e compartilhe facilmente dados existentes nos formatos Delta Lake e Apache Parquet para qualquer plataforma de dados.Compartilhamento de dados em tempo real sem replicaçãoCompartilhe dados em tempo real entre plataformas, nuvens ou regiões sem precisar replicá-los ou copiá-los para outro sistema.Governança centralizadaCentralize o gerenciamento, controle, auditoria e rastreamento do uso de dados compartilhados em uma única plataforma.Marketplace para produtos de dadosCrie e entregue produtos de dados, incluindo conjuntos de dados, modelos de ML e notebooks uma vez e distribua livremente em um marketplace central.Salas limpas de dados que protegem a privacidadeColabore facilmente com seus clientes e parceiros em qualquer nuvem por meio de um ambiente seguro, protegendo a privacidade dos dados.Como funcionaIntegração nativa com a plataforma da DatabricksA integração nativa com o Unity Catalog permite gerenciar e auditar centralmente os dados compartilhados entre as organizações. Ela também permite que você compartilhe com confiança ativos de dados com fornecedores e parceiros para uma melhor coordenação dos seus negócios, atendendo aos critérios de segurança e conformidade.Gerencie compartilhamentos facilmenteCrie e gerencie provedores, destinatários e compartilhamentos com uma interface de usuário fácil de usar, comandos SQL ou APIs REST com suporte completo para CLI e Terraform.Descubra e acesse produtos de dados por meio de um marketplace abertoDescubra, avalie e acesse com facilidade produtos de dados, incluindo conjuntos de dados, modelos de machine learning, dashboards e notebooks, de qualquer lugar, sem precisar estar na plataforma da Databricks.Salas limpas de dados que protegem a privacidadeColabore com seus clientes e parceiros em qualquer nuvem em um ambiente seguro. Compartilhe dados de seus data lakes com segurança, sem replicação. Encontre colaboradores na nuvem de sua preferência e ofereça a flexibilidade de executar cálculos complexos e cargas de trabalho em qualquer linguagem: SQL, R, Scala, Java e Python. Guie colaboradores em casos de uso comuns utilizando modelos pré definidos, notebooks e dashboards para acelerar o tempo até insights.Casos de usoCompartilhamento entre diferentes divisões internasCrie um Data Mesh com Delta Sharing para compartilhar dados com segurança entre diferentes unidades de negócios e subsidiárias em nuvens ou regiões sem copiar ou replicar os dados.Compartilhamento B2BMonetização de dadosClientes“O Delta Sharing nos ajudou a simplificar nosso processo de entrega para grandes conjuntos de dados. Assim, nossos clientes podem usar seu próprio ambiente de computação para consultar dados recentes, classificados e selecionados com pouco ou nenhum esforço de integração. Quanto a nós, podemos continuar expandindo nosso catálogo de produtos de dados únicos e de alta qualidade.”— William Dague, Head de dados alternativos“Como uma empresa orientada por dados, devemos garantir que nossos clientes tenham acesso aos nossos conjuntos de dados. A Plataforma Databricks Lakehouse com o Delta Sharing se alinha perfeitamente a esse processo para nos permitir alcançar com segurança uma base de usuários muito maior, independentemente da nuvem ou plataforma utilizada.”— Felix Cheung, vice-presidente de engenharia“Com os poderosos recursos do Delta Sharing da Databricks, a Pumpjack Dataworks tem uma experiência de onboarding mais suave. Não precisamos mais exportar, importar e remodelar os dados: para nossos clientes, isso é um valor agregado imediato. De fato, a aceleração dos resultados gera mais oportunidades de negócios para nossos clientes e seus parceiros.”— Corey Zwart, diretor de engenharia“Com o Delta Sharing, nossos clientes têm acesso a conjuntos de dados selecionados quase instantaneamente e podem integrá-los às ferramentas analíticas de sua escolha. As conversas com nossos clientes estão ficando mais ricas: discussões de alto nível sobre análises substituem as sessões técnicas de perguntas e respostas sobre a ingestão de dados e nos permitem criar experiências bem-sucedidas para os clientes. Nosso relacionamento com os clientes está mudando. Estamos nos esforçando para fornecer novos conjuntos de dados e atualizar os existentes por meio do Delta Sharing para manter os clientes a par das principais tendências em seus setores.”— Anup Segu, tech lead de engenharia de dadosUm ecossistema abertoAcesse a versão publicada mais recente diretamente do provedor, em ferramentas fáceis de usar com SQL, Python ou BI.RecursosApresentações e webinars[Apresentação] Governança de dados e compartilhamento de lakehouse no Data + AI Summit 2022[Webinar sob demanda] Acelere o valor comercial com o Delta SharingBlogsAnúncio da disponibilidade geral do Delta SharingDelta Sharing: Um protocolo aberto para o compartilhamento seguro de dadosTrês principais casos de uso para compartilhar dados com o Delta SharingVisão geral da solução e e-books[e-book] Explore a nova solução de Delta SharingDelta Sharing: Um padrão aberto para o compartilhamento seguro de dadosAscensão do Data Lakehouse por Bill Inmon, pai do data warehouseTudo pronto para começar a usar a Databricks?Experimente o Databricks gratuitamenteProdutoVisão geral da plataformaPreçosTecnologia de código abertoExperimente DatabricksDemoProdutoVisão geral da plataformaPreçosTecnologia de código abertoExperimente DatabricksDemoAprendizagem e suporteDocumentaçãoGlossárioTreinamento e certificaçãoCentral de ajudaInformações legaisComunidade onlineAprendizagem e suporteDocumentaçãoGlossárioTreinamento e certificaçãoCentral de ajudaInformações legaisComunidade onlineSoluçõesPor setorServiços profissionaisSoluçõesPor setorServiços profissionaisEmpresaQuem somosCarreiras em DatabricksDiversidade e inclusãoBlog da empresaEntre em contatoEmpresaQuem somosCarreiras em DatabricksDiversidade e inclusãoBlog da empresaEntre em contatoSee Careers at DatabricksMundialEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Aviso de privacidade|Termos de Uso|Suas opções de privacidade|Seus direitos de privacidade na Califórnia
https://www.databricks.com/de/trust?itm_data=menu-item-securitytrustcenter
Security & Trust Center – DatabricksSkip to main contentPlattformDie Lakehouse-Plattform von DatabricksDelta LakeData GovernanceData EngineeringDatenstreamingData-WarehousingGemeinsame DatennutzungMachine LearningData SciencePreiseMarketplaceOpen source techSecurity & Trust CenterWEBINAR 18. Mai / 8 Uhr PT Auf Wiedersehen, Data Warehouse. Hallo, Lakehouse. Nehmen Sie teil, um zu verstehen, wie ein Data Lakehouse in Ihren modernen Datenstapel passt. Melden Sie sich jetzt anLösungenLösungen nach BrancheFinanzdienstleistungenGesundheitswesen und BiowissenschaftenFertigungKommunikation, Medien und UnterhaltungÖffentlicher SektorEinzelhandelAlle Branchen anzeigenLösungen nach AnwendungsfallSolution AcceleratorsProfessionelle ServicesDigital-Native-UnternehmenMigration der Datenplattform9. Mai | 8 Uhr PT   Entdecken Sie das Lakehouse für die Fertigung Erfahren Sie, wie Corning wichtige Entscheidungen trifft, die manuelle Inspektionen minimieren, die Versandkosten senken und die Kundenzufriedenheit erhöhen.Registrieren Sie sich noch heuteLernenDokumentationWEITERBILDUNG & ZERTIFIZIERUNGDemosRessourcenOnline-CommunityUniversity AllianceVeranstaltungenData + AI SummitBlogLabsBaken26.–29. Juni 2023 Nehmen Sie persönlich teil oder schalten Sie für den Livestream der Keynote einJetzt registrierenKundenPartnerCloud-PartnerAWSAzureGoogle CloudPartner ConnectTechnologie- und DatenpartnerTechnologiepartnerprogrammDatenpartner-ProgrammBuilt on Databricks Partner ProgramConsulting- und SI-PartnerC&SI-PartnerprogrammLösungen von PartnernVernetzen Sie sich mit validierten Partnerlösungen mit nur wenigen Klicks.Mehr InformationenUnternehmenKarriere bei DatabricksUnser TeamVorstandUnternehmensblogPresseAktuelle Unternehmungen von DatabricksAuszeichnungen und AnerkennungenKontaktErfahren Sie, warum Gartner Databricks zum zweiten Mal in Folge als Leader benannt hatBericht abrufenDatabricks testenDemos ansehenKontaktLoginJUNE 26-29REGISTER NOWSecurity & Trust CenterYour data security is our priority    OverviewTrustSecurity FeaturesArchitectureCompliancePrivacyReport an IssueWir wissen, dass Daten zu Ihren wertvollsten Gütern gehören und jederzeit geschützt werden müssen. Deshalb ist Sicherheit in jeden Layer der Lakehouse-Plattform von Databricks integriert. Dank unserer Transparenz können Sie Ihre rechtlichen Anforderungen erfüllen und gleichzeitig die Vorteile unserer Plattform nutzen.Wir wissen, dass Daten zu Ihren wertvollsten Gütern gehören und jederzeit geschützt werden müssen. Deshalb ist Sicherheit in jeden Layer der Lakehouse-Plattform von Databricks integriert. Dank unserer Transparenz können Sie Ihre rechtlichen Anforderungen erfüllen und gleichzeitig die Vorteile unserer Plattform nutzen.Vertrauenswürdige PlattformVertrauen entsteht durch Transparenz. Finden Sie heraus, wie wir mithilfe branchenweit führender Praktiken (z. B. Penetrationstests, Sicherheitslückenmanagement und sichere Softwareentwicklung) die Databricks Lakehouse-Plattform zuverlässig absichern.Mehr InformationenSicherheitsmerkmaleWir implementieren umfassende Sicherheitsfunktionen zum Schutz Ihrer Daten und Workloads, beispielsweise Verschlüsselung, Netzwerkkontrollen, Audits, Identitätsintegration, Zugriffssteuerung und Data Governance.Mehr InformationenComplianceBranchenübergreifend vertrauen Kunden auf der ganzen Welt auf die Lakehouse-Plattform von Databricks. Wir verfügen über alle erforderlichen Zertifizierungen und Bescheinigungen, um auch die speziellen Anforderungen stark regulierter Branchen zu erfüllen.Mehr InformationenDatenschutzWir würdigen den Schutz Ihrer Daten und wissen, dass er für Ihr Unternehmen und Ihre Kunden gleichermaßen zwingend notwendig ist. Databricks unterstützt Sie bei der Einhaltung von Datenschutzgesetzen und der Erfüllung behördlicher Anforderungen.Mehr InformationenMit unserem Due-Diligence-Paket, das Dokumentations- und Compliance-Materialien enthält, führen Sie Sicherheitsüberprüfungen von Databricks selbst durch.Due-Diligence-PaketProduktPlatform OverviewPreiseOpen Source TechDatabricks testenDemoProduktPlatform OverviewPreiseOpen Source TechDatabricks testenDemoLearn & SupportDokumentationGlossaryWEITERBILDUNG & ZERTIFIZIERUNGHelp CenterLegalOnline-CommunityLearn & SupportDokumentationGlossaryWEITERBILDUNG & ZERTIFIZIERUNGHelp CenterLegalOnline-CommunityLösungenBy IndustriesProfessionelle ServicesLösungenBy IndustriesProfessionelle ServicesUnternehmenÜber unsKarriere bei DatabricksDiversität und InklusionUnternehmensblogKontaktUnternehmenÜber unsKarriere bei DatabricksDiversität und InklusionUnternehmensblogKontaktWeitere Informationen unter „Karriere bei DatabricksWeltweitEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Datenschutzhinweis|Terms of Use|Ihre Datenschutzwahlen|Ihre kalifornischen Datenschutzrechte
https://www.databricks.com/dataaisummit/speaker/shawn-benjamin
Shawn Benjamin - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingShawn BenjaminData and Business Intelligence Chief at U.S. Department of Homeland Security - USCISBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/maria-daibert
Maria Daibert - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingMaria DaibertData Platform Product Manager at AB InbevBack to speakersData Platform Product Manager at Anheuser-Busch InBev (Brazil), with 5+ years of expertise in product management, business development and agile frameworks. Passionate about Big Data, strategy and agility. A Data Mesh enthusiast.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/gilad-asulin
Gilad Asulin - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingGilad AsulinBig Data Engineer Team Leader at Akamai Technologies, IncBack to speakersGilad Asulin is a Senior Big Data Team Leader at Akamai. He has over 20 years of experience in development and software architecture. Gilad specialties are Big Data, cloud security and cloud technologies.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/solutions/accelerators/demand-forecasting
Demand Forecasting at Scale | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWSolution AcceleratorBuild Demand Forecasts at ScalePre-built code, sample data and step-by-step instructions ready to go in a Databricks notebookGet startedDemand forecasting is the process of leveraging historical data and other analytical information to build models that help predict future estimates of customer demand for specific products over a specific period. It helps shape product road map, inventory production and inventory allocation, among other things.According to McKinsey, a 10% to 20% improvement in supply chain forecasting accuracy is likely to produce a 5% reduction in inventory costs and a 2% to 3% increase in revenues. In a world where margins are increasingly narrow and critical, this percentage can be make or break. But traditional supply chain forecasting tools have failed to deliver the desired results, limiting success of retailers and manufacturers.Generate fine-grained forecasts at the retail store level in less timePerform fine-grained forecasting at the store-item level in an efficient manner, leveraging the distributed computational power of the Databricks Lakehouse Platform. This accelerator helps retailers overcome the technical limitations of legacy data analytics solutions that undermine forecasting accuracy. Instead, perform full forecasts on atomic-level data within tight service windows to do things like:Construct a forecast for each store-item combinationProject demand for each product across storesAs new sales data arrives, efficiently generate new forecasts and append existing forecastsWork in either Python or RDownload notebookIntermittent demand forecasting with NixtlaFine-grained forecasting often exposes patterns of intermittent demand. These patterns require specialized techniques to produce forecasts for goods that do not move at a regular, easily predictable cadence.In this accelerator, built with our partners at Nixtla, we demonstrate how these techniques can be employed to produce:Scalable, accurate forecasts across large numbers of store-item combinations experiencing intermittent demandAutomated model selection, aka model bake-offs, to ensure the best model is selected for each store-item combinationMetrics that help us identify the optimal frequency with which to generate new predictionsDownload notebookForecast demand at the part level for streamlined manufacturingPerform demand forecasting at the part level rather than the aggregate level to minimize disruptions in your supply chain and increase sales. Leverage this accelerator to:Build fine-grained demand forecasting that can be scalably performed on a more frequent basisManage material shortages and predict overplanningDownload notebookResources All the resources you need. All in one place. Explore the resource library to learn how to forecast demand at scale. Explore resourceseBooks & VideosLearn moreExplainer VideoLearn moreBlogLearn moreDeliver innovation faster with Solution Accelerators for popular data and AI use cases across industries. See our full library of solutionsReady to get started?Try Databricks for freeProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/company/partners/consulting-and-si/partner-solutions/accenture-unified-view-demand
Accenture Unified View of Demand | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWBrickbuilder SolutionUnified View of Demand by AccentureIndustry-specific solution developed by Accenture and powered by the Databricks Lakehouse PlatformGet startedGenerate more accurate forecasts in less timeTo drive growth and innovation, leading retail and CPG companies are pivoting their supply chain to become more customer-centric. Accenture Unified View of Demand is an open, glass box approach to demand planning. Maximize accuracy, granularity and timeliness with a single-source-of-truth demand plan while also enabling better explainability and alignment across various functions. With Accenture Unified View of Demand, you can:Increase forecast accuracy, speed and granularityShift from debating forecasts to input alignmentScale patterns for consumption-led forecastingGet startedProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/de/databricks-ventures
Databricks Ventures | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWDatabricks VenturesInvesting in the future of data, analytics and AI Databricks Ventures invests in innovative companies that share our view of the future for data, analytics and AI. Our inaugural initiative, the Lakehouse Fund, is focused on early and growth-stage companies that are extending the lakehouse ecosystem or using the lakehouse architecture to create the next generation of data and AI-powered companies.Portfolio company benefitsExclusive insight into the product roadmap and support for building deep technical integrationsGuidance and best practices from the network of Databricks mentorsBroader reach by partnering with Databricks go-to-market programsLakehouse Fund investment focusA shared vision to build the future of data, analytics and AI on the lakehouse architectureInvestments in early to growth-stage companies in partnership with a lead investorProjects and entrepreneurs that share Databricks’ commitment to open source and building on open platforms, much like the Databricks LakehouseOur PortfolioAlation is the leader in enterprise data intelligence solutions. It helps organizations transform raw data into actionable insights with its data intelligence platform that empowers users to find, understand, govern and use data collaboratively. Learn moreArcion is a cloud-native, distributed CDC-based data replication platform that makes building real-time data pipelines simple. Arcion helps enterprises eliminate brittle pipelines and data silos. Learn moredbt is a data transformation framework. Users can work directly within their data lakehouse or warehouse to quickly produce trusted data sets for reporting, ML modeling and operational workflows. Learn moreHex is a platform for collaborative analytics and data science. Users can connect to data, analyze in a collaborative SQL and Python-powered notebook, and share work as interactive data apps that anyone can use.Learn moreThe Hunters SOC platform empowers security teams to automatically identify and respond to incidents that matter, helping teams mitigate real threats faster and more reliably than SIEMs.Learn moreThe Immuta Data Security Platform enables organizations to unlock value from their cloud data by protecting it and providing secure access. The platform provides sensitive data discovery, security and access control, and data activity monitoring.Learn moreLabelbox is building a collaborative training data platform that makes it easy to create and manage labeled data, enabling rapid deployment of AI applications.Learn moreMatillion is the data productivity cloud that gets data business-ready, faster — with enterprise-scale load, transform, sync and orchestration — for insights, analytics, data science, machine learning and AI.Learn morePerplexity advances the way people discover and share information using AI-powered search — providing instant answers and information on any topic, with up-to-date sources to help people discover, research and learn faster.Learn moreRevelate’s data fulfillment platform provides a suite of capabilities for data sharing and data commercialization for customers to fully realize the value of their data.Learn moreTecton, the machine learning feature platform company, enables data teams to build, centralize, share, and serve production-ready ML features for offline training and online inference, at scale.Learn moreFAQsWhy partner with Databricks Ventures? Depending on the size and stage of the investment, Databricks will provide unique access to product roadmap direction, technical integration support, and go-to-market support; access to Databricks mentors for guidance and best practices; and exposure in Databricks’ global go-to-market programs, including our Data + AI Summit series.What are Databricks Ventures’ investment criteria? We are investing in innovative founders of private companies who share our view of the future for data and AI and are committed to extending the lakehouse ecosystem or using the lakehouse architecture to create the next generation of data and AI-powered companies.What stages does Databricks invest in? We invest in early through growth-stage private companies within the data, analytics and AI ecosystem.Does Databricks Ventures lead investment rounds? No, we do not lead rounds. Our strategy is to invest alongside a lead institutional investor.What is Databricks Ventures’ typical investment size? Databricks Ventures takes minority positions in rounds led by institutional VCs; typical check size varies based on stage of company and size of funding round.Does Databricks take board seats?No, Databricks does not plan to take board seats in portfolio companies.Build your startup on DatabricksDatabricks for Startups offers free credits, expert advice and go-to-market support.Learn moreProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/dataaisummit/speaker/varun-sharma
Varun Sharma - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingVarun SharmaPrincipal Data Engineer at Visa Inc.Back to speakersVarun Sharma is a Principal Data Engineer at Visa's Data and AI platform, with over a decade of Big Data experience in the finance domain. At Visa, Varun has been instrumental in building a high-performance data engineering platform to power the company's analytics and machine learning applications. Varun has a deep understanding of distributed computing like frameworks like Apache Spark and Hadoop. He's very passionate about making data accessible to everyone.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/glossary/elasticsearch
What is Spark Elasticsearch?PlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWSpark ElasticsearchAll>Spark ElasticsearchTry Databricks for freeGet StartedWhat is Spark Elasticsearch?Spark Elasticsearch is a NoSQL, distributed database that stores, retrieves, and manages document-oriented and semi-structured data. It is a GitHub open source, RESTful search engine built on top of Apache Lucene and released under the terms of the Apache License. Elasticsearch is Java-based, thus available for many platforms that can search and index document files in diverse formats. The data stored in Elasticsearch is in the form of schema-less JSON documents; similar to NoSQL databases. The history of and an introduction to ElasticsearchAn integral aspect of a larger set of open-source tools known as the Elastic Stack, Elasticsearch is a popular full-text search engine, originally designed and developed by engineers at the Google Brain Team. It's widely used in a number of commercial applications, from Reddit, to YouTube to eBay. For many companies, text-based search has become an essential component of their business processes. In this way, Elasticsearch is similar to other search engines. One key difference between Elasticsearch and other search engines is that Elasticsearch can store and manage distributed data. In other words, it's designed to deal with data that has a constantly varying size. This provides the capability for very complex queries, no matter how large a data set is. However, the potential impact of making a single database server handle data from multiple users can increase significantly.Who uses Elasticsearch?Thousands of top companies use Elasticsearch for both their online and offline data, including tech giants like Google, Oracle, Microsoft and many other household names. But you don't have to be a tech giant to want an easy way to index structured data. You just have to know it exists and understand how it works.But what is Elasticsearch used for, exactly?Elasticsearch can serve a broad range of use cases, such as:Logging and Log Analysis:  The ecosystem of complementary open source software and platforms built up around Elasticsearch has made it one of the easiest to implement and scale logging solutions.Scraping and Combining Public Data: Elasticsearch is flexible enough to take in multiple different sources of data and keep it all manageable and searchable.Full-Text Search: ElasticSearch is document-oriented. It stores and indexes documents. Indexing creates or updates documents. Once the indexing is finished, you can search, sort, and filter complete documents—not rows of columnar data.Event Data and Metrics: Elasticsearch is also known for working great well on time-series data such as metrics and application events. No matter the technology you are using, Elasticsearch probably has the needed components to easily grab data for common applications; and in the rare case that it doesn't, adding that capability is quite easy.Elasticsearch architecture: key componentsTo understand how Spark Elasticsearch works, when to use it and when not to use it, you have to first understand the infrastructure behind the Elasticsearch architecture. These key components include everything from the Elasticsearch cluster, ports 9200 and 9300, and Elasticsearch shards to Elasticsearch replicas, analyzers and documents.Elasticsearch clusterAn elasticsearch cluster is a group of interconnected computing nodes, all of which store different pieces of cluster data. As a user, you can adjust the number of nodes each cluster is assigned to run by altering the "elasticsearch.yml" file found in the configurations folder. While it's possible to run as many clusters as you'd like, most users typically find one node is all it takes to achieve their desired results.Elasticsearch nodeAn Elasticsearch node is a computing resource that is specifically tuned for searching, indexing and scaling the database. Since Elasticsearch is a distributed database, it uses a single source of truth, which is the Elasticsearch data node that holds all of your data. Each node in a cluster uses a different name. Typically, Elasticsearch nodes have about 10 to 50 million documents in each index.Ports 9200 and 9300There are two types of ports available on Elasticsearch shard. The first of the two shard ports is always open, and the second shard port is opened only when an Elasticsearch index is created and a cluster is initialized. 9200 is the default port to use for the primary shard and 9300 is the default port to use for its replica.Elasticsearch shardsElasticsearch shards are simply a collection of Kibana indexes inside one index. There are two types of index in Elasticsearch, Elasticsearch documents (doc) and Elasticsearch indexes. Documents are bound with identifiers and indexes with a unique name.Elasticsearch replicasA replica is a copy of a shard with all changes being reflected on the secondary replica but remaining transparent to the client. The primary replica is updated automatically when new data is added or when deleted, updated or modified.Elasticsearch analyzersAn analyzer is a part of an Elasticsearch cluster that fetches data from the database and performs analysis with it. This makes it possible to filter and sort the data that's been returned to the user.Elasticsearch documentThe Elasticsearch documents are the primary index type in Elasticsearch. Each document is created as an ID in the data set and has a single column per document type. A simple example of a document ID for Elasticsearch is {doc id}. In general, each document in an Elasticsearch cluster has a shard ID, name and an array of indexes with all the fields having their own shard-wide identifiers.How does Elasticsearch work?In short, Elasticsearch works by taking data and publishing it on to every node in the cluster, and then scaling data up and down based on the current amount of data being stored. Elasticsearch benefits from being able to store all of your data in one database, with an elastic index container. Because Elasticsearch is a document-oriented, RESTful search engine, it has a variety of useful tools and can work with large, and otherwise intimidating, data sets. Additionally, this software can be used as a complementary tool in addition to another. For instance, Elasticsearch + Spark.An Elasticsearch query exampleLet's say you wanted to search for the word "Telecommunications". The following simple search syntax will do the trick: $"Telecommunications" Since Elasticsearch works from documents, you can't simply search through a list of documents. You need to query a "document type". We'll use the phrase "type:Telecommunications" to ensure we only get documents that meet the search criteria. To make this query, we pass the document ID number as a query parameter: $"type:telecommunications" To test this further, you could also create a simple example document by running the following: create index:type:telecommunications create partition:type:telecommunications --data-urlencode /tasks --data-urlencode tasks/What type of database does Elasticsearch use?By combining the Lucene indexing build with a robust distribution database model, the Elasticsearch tool is able to fragment data sets into small components known as shards and distribute them across various nodes.But where does Elasticsearch store data?The data stored in Elasticsearch is either in JSON format or CSV format. Every index has its own template for documents stored in the index. The index is fully-replicated using a message bus to communicate with the secondary replication. Log files are written as Elasticsearch indexes. These documents are stored as an array of key-value pairs in a data structure known as a "memcached set". A memcached set is a lightweight, low-memory, scalable data structure and has the ability to hold and process data with a large memory volume. Elasticsearch's storage is optimized for ingest, indexing and search operations with files being written to the disk at regular intervals. In fact, the only way to change the index's size is to delete the last inserted document and replace it with a new one. This task is called "data migration" and refers to a new document being created from the new index, updated and then re-inserted.What is Elasticsearch aggregation?Elasticsearch aggregation, or allocating the same cluster to multiple endpoints, is a powerful feature that allows you to use the same Elasticsearch cluster for additional data and functionality without affecting the performance of your production cluster. When aggregating clusters, each node is assigned one of three different workload types. The workload types are:Non-relationalOnline transaction processing (OLTP)Online analytical processing (OLAP)Non-relationalAll of the network requests generated by Elasticsearch are generated by queries that are being run against the Elasticsearch cluster. When an Elasticsearch node is idle, it is the responsibility of the operating system to run queries on a background thread and continuously report on the results. When an Elasticsearch node is being used, it will participate in a failover mechanism (in the event of a node failure) or (in the case of node overload) it will pass the query requests through to a number of other nodes, waiting until one of the other nodes is free. While the network traffic generated by Elasticsearch is most commonly querying related data, there are many other situations that can also take advantage of Elasticsearch.OLTPAll of the network requests generated by Elasticsearch are still generated by queries that are running against the Elasticsearch cluster. While this can be a full Elasticsearch cluster for a large system (and certainly a good start), there are times when it's desirable to combine Elasticsearch with a relational data source. In those cases, Elasticsearch will be running against a secondary relational data source for processing and will only keep track of some of the queries it has run. In this scenario, each node is assigned only one secondary source, with the other remaining idle.OLAPA key distinction between Elasticsearch aggregation and regular aggregation is that, while other aggregations can use the same Elasticsearch cluster for multiple purposes, Elasticsearch aggregation uses a secondary data source to store and process the aggregated data. This allows for Elasticsearch aggregation to store more data without creating additional queries on a primary dataset, such as SQL or NoSQL data sets.How to install Elasticsearch and use itInstallation is fairly straightforward. It's possible to use default repositories for Elasticsearch and set a default environment for Elasticsearch, too. Elasticsearch uses a configuration file called Kibana.yml as the basis for its configuration. You can modify the file to suit your needs. You can also use any of the more popular Elasticsearch plugin providers such as InfluxDB, Logstash, etc.The steps to installing Elasticsearch:Install the Elasticsearch Development version as well as the server and install dependenciesInstall the BOSH Extension for Java. The BOSH extension helps you write HTML templates for your Elasticsearch to make data more accessible and readable for humans as well as data manipulation tools. The BOSH extension requires a Java runtime. You can use default repositories for Java on your operating system to install it.Launch ElasticsearchInstall the Java plugin for BOSHThat's it. Elasticsearch is up and running on your machine and you now have access to all of the data in an easy to read way.Elasticsearch data visualizationElasticsearch allows you to search and filter through all sorts of data via a simple API. The API is RESTful, so users can not only use it for data-analysis but can also use it in production for web-based applications. Currently, Elasticsearch includes faceted search, a functionality that allows you to compute aggregations of your data. Here are some of the most relevant features:It provides a scalable search solution.Performs near-real-time searches.Provides support for multi-tenancy.Streamlines backup processes and ensures data integrity.An index can be recovered in case of a server crash.Uses Javascript Object Notation (JSON) as well as Java application program interfaces (APIs).Automatically indexes JSON documents.Indexing uses unique type-level identifiers.Each index can have its own settings.Searches can be done with Lucene-based query strings.Why use Elasticsearch instead of SQL?The Elasticsearch service is by far the most widely adopted, powerful and useful search technology because when it comes to processing large amounts of data quickly and efficiently, it's notably better than most of its SQL counterparts. Elasticsearch is purpose-built for enterprise search use, providing powerful features and ease of use tools to businesses that rely on data analytics. Thus offering them a more practical and flexible way to store, search and analyze batches of data in a less resource-intensive way.How to check Elasticsearch versionThere are two quick ways to check the version of Elasticsearch you're running. The first is to launch and login to your ElasticSearch console and view your software version. The second is to check your Elasticsearch official documentation.Top three Elasticsearch alternatives on the marketWhen considering which software to use, there are three top Elasticsearch alternatives to take into account before making your decision:AWSAmazon Web Services (AWS) has become the top computing platform for startups, cutting-edge research and the biggest enterprises looking to enhance their computing infrastructure. With technology that allows customers to use and build their own virtual servers, along with the industry's widest set of cloud-computing services, AWS powers the so-called "cloud wars" between Microsoft's Azure and Google's GCP.SolrApache Solr is an open source (BSD licensed) search analytics engine daemon written in Java and is one of the most popular open-source search engines. In fact, Solr powers the search functionality of many of the world's largest e-commerce sites and social media platforms including Twitter, Yahoo, Amazon, eBay and eBay Enterprise. Solr uses a distributed architecture to provide rapid search, and features a unique unified Storage API that enables the search engine to integrate seamlessly with virtually any storage mechanism used by the enterprise.ArangoDBArangoDB is a distributed, NoSQL document-oriented database and has become a popular choice due to its powerful data analytical processing and ease-of-use. It's an SQL-like language that operates over the ArangoDB key-value store, allowing users to create tables, joins and queries the same way they would in relational databases. ArangoDB does a good job of keeping all of its code up to date, and the support pages are well designed. As the project matures and more people contribute, you can expect these pages to stay up to date and easy to navigate. Not to mention, it's compatible with all the major programming languages like Python and Javascript.The three best Elasticsearch toolsTo make the most of your data, we recommend using Elasticsearch in tandem with other tools and software—most notably Hevo Data, Logstash, and Apache Nifi.Hevo DataHevo Data Elasticsearch is a free, open-source distributed search engine designed to ingest Elasticsearch data, parse it into queries and run them as event logs on the cluster nodes. The software lets you run analytics queries in real time on real-time data as well as backups of that data.LogstashSimply put, Logstash is an Elasticsearch tool that allows you to define rules that help manage incoming data as soon as it's extracted by Elasticsearch. By taking the data and instantly processing it, Logstash provides analytical and visualization tools perfect for making the most out of your data.Apache NiFiApache Nifi is a set of libraries that enables "deep linking" between multiple data sources, including but not limited to popular Open Source APIs such as Facebook's Core Location API, Twitter's REST APIs, and even Yelp's In-App Feature API. With Apache NiFi, users are able to link their own APIs and make all of a dataset's information available to various other software.Is Elasticsearch right for you?With everything you know now about Elasticsearch, from its capabilities to its infrastructure and architecture, all that's left is deciding whether it's an ideal tool for your business.Additional ResourcesElasticSearch DatasetsScalable Time Series Forecasting and Monitoring using Apache Spark and ElasticSearch at AdyenBuilding a Dataset Search Engine with Spark and ElasticsearchBack to GlossaryProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. 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Unlocking the Power of Data: AT&T’s Modernization Journey to the Lakehouse - The Databricks BlogSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCategoriesAll blog postsCompanyCultureCustomersEventsNewsPlatformAnnouncementsPartnersProductSolutionsSecurity and TrustEngineeringData Science and MLOpen SourceSolutions AcceleratorsData EngineeringTutorialsData StreamingData WarehousingData StrategyBest PracticesData LeaderInsightsIndustriesFinancial ServicesHealth and Life SciencesMedia and EntertainmentRetailManufacturingPublic SectorUnlocking the Power of Data: AT&T’s Modernization Journey to the Lakehouseby Kate HopkinsApril 11, 2022 in Company BlogShare this postThis is a guest post from Kate Hopkins, Vice President of Data Platforms at AT&T.The beginning: Moving and managing petabytes of data with careAT&T started its data transformation journey in late 2020 with a sizable mission: to move from our core on-premises Hadoop data lake to a modernized cloud architecture. Our strategy was to empower data teams by democratizing data, as well as scale AI efforts without overburdening our DevOps team. We saw enormous potential in increasing our use of insights for improving the AT&T customer experience, growing the AT&T business and operating more efficiently.While some businesses might accomplish smaller migrations more readily, we at AT&T had a lot to consider. Our data platform ecosystem of technologies ingests over 10 petabytes of data per day, and we manage over 100 million petabytes of data across the network. It was extremely important for us to take our time selecting the right tool for the job. Not only because of the large data volumes but also because we have 182 million wireless subscribers and 15 million broadband households to support who are using data. In addition, we have important systems that protect our customers against breaches and fraud. Essentially, we needed to democratize our data in order to use it to its full potential but balance that democratization with privacy, security, and data governance.Our legacy architecture, which includes over six different data management platforms, enabled data teams to work closely with data and act on it quickly. But at the same time, it locked those efforts in silos. These distributed pockets of work led to challenges accessing and acquiring data, as well as data duplication and latency issues. Without a single truth from which to draw information, metrics were created out of different versions of data that reflected different points in time and levels of quality.Ultimately, to realize the data-driven innovations we desired, we needed to modernize our infrastructure by moving to the cloud and adopting a data architecture built on the premise of open formats, simplicity, and cross-team collaboration. We chose Databricks Lakehouse as a critical component for this monumental initiative.Accessible data leads to better insights and a center of excellence2021 was all about getting AT&T’s on-premises data into the Databricks Lakehouse Platform. I’m excited to say that with Lakehouse as our unified platform, we’ve successfully moved all our core data lake data to the cloud.Our data science team, who were the first adopters, adjusted to this change with ease. They have since been able to move their machine learning (ML) workloads to the cloud. This has enabled faster data retrieval, more data (if you can believe it), and accessibility to modernized technologies that have brought fraud down to the lowest level in years. For example, we’ve been able to train and deploy models that detect fraudulent phone purchase attempts and then communicate that fraud across all channels to stop it completely. We’ve also seen a significant increase in operational efficiency, a reduction in customer churn, and an increase of customer LTV.Within CDO, we’ve been onboarding a large data engineering and data science community. We’re ingesting both structured customer data into Delta Lake, as well as a large amount of raw, unstructured, real-time data to help continue powering these important use cases.But the value doesn’t stop at our ability to scale data science. Our business users have also been able to extract data insights through integrations that run Power BI and Tableau dashboards off the data in Delta Lake. The sales organization uses data-driven insights fed through Tableau to uncover new upsell opportunities. They are also able to generate recommendations on ideal responses based on the questions customers are asking.Most importantly, moving to Databricks Lakehouse has enabled AT&T to move to the analytics center of excellence (COE) model. As we decentralize our technology team to support businesses more closely, we’re ultimately aiming to empower each business unit to serve themselves. This includes knowing who to reach out to if they have a question, where to find training, how to get a deeper understanding of how much they’re spending, and more. And for all of those reasons, the center of excellence has been key. It’s led to greater product adoption, and so much meaningful trust and appreciation from our partners.Retiring on-prem entirely, making cost-saving gains, and accelerating success in 2022In 2020, we succeeded in making the case and proving the benefits of moving to the cloud. The ability to rapidly execute our transformation plan helped us exceed our savings targets for 2021, and I'm expecting to do the same in 2022. The real win, however, is going to be the increased business benefits we expect to see this year as we continue moving our data over to Delta Lake so we can retire our on-prem system entirely.This move will enable us to do really exciting things, like standardize our artificial intelligence (AI) tooling, scale data science and AI adoption across the business, support business agility through federation, and leverage more capabilities as our roadmap evolves.I’m certain that the Databricks Lakehouse architecture will enable our future here at AT&T. It’s the target architecture for our AI use cases, and we are confident it will increase our business agility because in less than a year we have already seen the results of the federation and business value it enables. Critically, it also supports required data security and the governance for a single version of truth across our complex data ecosystem.Related Content: AI Modernization at AT&T and the Application to Fraud with DatabricksTry Databricks for freeGet StartedSee all Company Blog postsProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. 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https://www.databricks.com/dataaisummit/speaker/nicolas-pelaez/#
Nicolas Pelaez - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingNicolas PelaezTechnical Marketing at DatabricksBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
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Migrate from Hadoop to Databricks | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWMigrate from HadoopMove your legacy Hadoop platform to the Databricks Lakehouse Try Databricks freeContact DatabricksHadoop has proven unscalable, overly complex and unable to deliver on innovative use cases. Migrating from Hadoop to Databricks will help you scale effectively, simplify your data platform and accelerate innovation with support for analytics, machine learning and AI.The challenge with HadoopOn-premises Hadoop deployments have significant limitations, including wasted hardware capacity, high DevOps burden, increased power costs and software version upgrades.Even if you migrate legacy on-premises Hadoop workloads to cloud-based managed services like EMR, HDInsight or Dataproc, you’ll still run into the same reliability and scalability issues. This leads to a lot of time being wasted on troubleshooting, infrastructure, resource management overhead and stitching different managed services on the cloud to maintain an end-to-end pipeline.To learn more about the challenges organizations face with on-premises as well as cloud deployments of Hadoop, check out these blogs:BlogIt’s Time to Re-evaluate Your Relationship With Hadoop Read moreBlog7 Reasons to Migrate From Your Cloud-Based Hadoop Read moreWhy migrate from Hadoop to the Databricks Lakehouse?Simplify your data platform Get a single modern platform for all your data, analytics and AI use cases. Unify governance and the user experience across clouds and data teams. Scale cost-effectively Stop managing servers, and scale on demand with serverless. Run data warehousing at scale with up to 12x better price/performance. Accelerate innovation Build AI, ML and real-time analytics capabilities faster with collaborative, self-service tools and open source technologies such as MLflow and Apache Spark™. Migrate with confidence Automated tools, technical guidance, partner solutions and professional services help you eliminate risk and accelerate your migration journey. Ready to take your first step?Contact DatabricksMigration resourcesWebinarStep-by-Step Guide to Hadoop Migration Watch noweBookMigrating From Hadoop to Data Lakehouse for Dummies Download nowTechnical GuideMigration Guide: Hadoop to Databricks Download nowBrickbuilder SolutionsLeverage Brickbuilder Solutions from leading consulting partners — built for migrating from Hadoop to the Databricks LakehouseLegacy System Migration by AvanadeMove your data to unlock its full value Get startedMigrate to Cloud and Databricks by CapgeminiStreamline data migration to the Databricks Lakehouse Platform Get startedMigrate to Databricks by Celebal TechnologiesQuicker migration from on-premises at lower cost Get startedLeapLogic Migration Solution by ImpetusAuto-transform ETL, data warehouse, analytics and Hadoop workloads to Databricks Get startedData Wizard for Hadoop/EDW Migrations by InfosysConfidently move your data to Databricks Get startedData Intelligence Suite by WiproMigrate with confidence to Databricks Get startedCustomers who have successfully migrated from cloud-based Hadoop to DatabricksCustomers who have successfully migrated from on-premises Hadoop to DatabricksMigrating from a data warehouse?Learn moreResourceseBooksMigrating From Hadoop to Data Lakehouse for DummiesMigration Guide: Hadoop to DatabricksThe Hidden Value of Hadoop Migration to a Cloud Data LakeThe Hidden Value of Hadoop Migration — AWSEventsStep-by-Step Guide to Hadoop MigrationHow to Break Up With Hadoop — Build a Future With a Data Platform Made for AI and BILife After HadoopData Lake Modernization — 10 Habits of Highly Effective Hadoop Migrations to Azure DatabricksBlogsIt’s Time to Re-evaluate Your Relationship With HadoopTop Considerations When Migrating Off of Hadoop5 Key Steps to Successfully Migrate From Hadoop to the Lakehouse Architecture7 Reasons to Migrate From Your Cloud-Based Hadoop to the Databricks Lakehouse PlatformThe Hidden Value of Hadoop MigrationGet startedMigration doesn’t have to be a headache. Contact us today to talk about what it could look like for you.Try Databricks freeContact DatabricksProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. 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Databricks on AWS Data Platform - DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWDatabricks on AWSThe simple, unified data platform seamlessly integrated with AWS  Get startedSchedule a demoDatabricks on AWS allows you to store and manage all your data on a simple, open lakehouse platform that combines the best of data warehouses and data lakes to unify all your analytics and AI workloads.Reliable data engineeringSQL analytics on all your dataCollaborative data scienceProduction machine learningWhy Databricks on AWS?Simple Databricks enables a single, unified data architecture on S3 for SQL analytics, data science and machine learning12x better price/performance Get data warehouse performance at data lake economics through SQL-optimized compute clustersProven Thousands of customers have implemented Databricks on AWS to provide a game-changing analytics platform that addresses all analytics and AI use casesDollar Shave Club: Personalizing customer experiences with Databricks Download eBookHotels.com: Optimizing the customer experience with machine learning Download case studyHP: From data prep to deep learning — how HP unifies analytics with Databricks Watch on-demand webinarFeatured integrationsAWS GravitonAWS GravitonDatabricks clusters support AWS Graviton instances. These instances use AWS-designed Graviton processors that are built on top of the Arm64 instruction set architecture. AWS claims that instance types with these processors have the best price/performance ratio of any instance type on Amazon EC2. Read moreAWS SecurityAmazon RedshiftAWS GlueEnterprise RolloutUse CasesPersonalized recommendation engines Process all your data in real time to provide the most relevant product and service recommendations.Genomic sequencing Modernize your technology stack to improve the experience for patients and physicians with the fastest DNASeq pipeline at scale.Fraud detection and prevention Leverage complete historical data together with real-time data streams to quickly identify anomalous and suspicious financial transactions.ResourcesWhitepapersMining Your Data Lake for Analytics InsightsUnifying Big Data and AI in the Financial Services IndustryThe Hidden Value of Hadoop MigrationWebinarsModernize Data and Analytics Platforms with Confidence with Databricks and AWSWhy Data-Focused Startups Are Building on the LakehouseHarness the Power of Real-Time Analytics for Rapid Decision-Making at QubyLoyaltyOne Simplifies and Scales Data Analytics Pipelines With Delta LakeUnlock the Potential Inside Your Data LakeBuilding a Data Lakehouse at DoorDash and GrammarlyIndustriesLeveraging AI/ML to Extract Real-World Insights From Population-Scale Clinical Lab Data at PrognosHow Machine Learning Is Changing Data Analytics in GovernmentReady to get started?Try Databricks for freeProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. 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https://www.databricks.com/dataaisummit/speaker/alain-briancon/#
Alain Briancon - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingAlain BrianconVP Data Science Profiles by Kantar at Kantar GroupBack to speakersAlain is Vice President of Data Science at Kantar Profiles decision. Alain is a serial entrepreneur and inventor with 77 patents. Over the last ten years, through various startups, Alain has applied data science to IoT, predicting appliance failures, political campaigns, food and diet management, customer engagement, upsell/cross-sells, and now surveys. Alain graduated from the Massachusetts Institute of Technology with a Ph.D. in Electrical Engineering and Computer Science. Outside work, he enjoys writing movie scripts, collecting fountain pens, and enjoying family and dogs.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
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Agenda - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingAgenda at a glanceExplore sessionsSTARTING AT $1,795In Person EventFREEVirtual EventMonday, June 26, 2023Hands-On Training Courses & Certification for Data Engineering, Machine Learning, LLMs, and more*IN PERSONVIRTUALTuesday, June 27, 2023Hands-On Training Courses & Certification for Data Engineering, Machine Learning, LLMs, and more*IN PERSONVIRTUALBreakout Sessions30010Partner SummitLightning Talks, AMA’s, & Meetups on topics such as Apache Spark ™, Delta Lake, MLflow, and moreWednesday, June 28, 2023KeynotesIN PERSONVIRTUALBreakout Sessions30010100+ leading data and AI companies in Dev Hub + ExpoIndustry Forums for Financial Services, Retail, Healthcare & Life Sciences, Media & Entertainment, Public Sector, and ManufacturingCertificationsClosing PartyThursday, June 29, 2023KeynotesIN PERSONVIRTUALBreakout Sessions100+ leading data and AI companies in Dev Hub + ExpoIndustry Forums for Financial Services, Retail, Healthcare & Life Sciences, Media & Entertainment, Public Sector, and ManufacturingCertificationsOn Demand Library HomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/sumesh-nair/#
Sumesh Nair - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingSumesh NairDirector of Engineering, Data and Cloud Strategy at Optum, United HealthGroupBack to speakersExperienced and Accomplished senior IT leader with a 20-year track record of excellence. An Agile enthusiast with a history of transforming organizations to optimize product engineering & delivery. A servant-leader known for building high-performing teams to discover new solutions, challenge norms, and gain competitive advantages in the market. An experienced leader with P&L Oversight background, multi-channel product leadership, and driving organizational change by exceeding expectations.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/company/careers/open-positions?location=amsterdam%2C%20netherlands
Current job openings at Databricks | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOW OverviewCultureBenefitsDiversityStudents & new gradsCurrent job openings at DatabricksDepartmentLocationProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/dataaisummit/speaker/binwei-yang/#
Binwei Yang - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingBinwei Yangsoftware engineer at intelBack to speakersBinwei Yang is a big data analytics architect at Intel, focusing on performance optimization of big data software, accelerator design and utilization in big data framework, as well as the big data and HPC framework integration. Prior to the big data role, Binwei worked in intel micro architecture team and focusing on performance simulation and analysis.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/fr/learn
Apprendre | DatabricksSkip to main contentPlateformeThe Databricks Lakehouse PlatformDelta LakeGouvernance des donnéesData EngineeringStreaming de donnéesEntreposage des donnéesPartage de donnéesMachine LearningData ScienceTarifsMarketplaceOpen source techCentre sécurité et confianceWEBINAIRE mai 18 / 8 AM PT Au revoir, entrepôt de données. Bonjour, Lakehouse. Assistez pour comprendre comment un data lakehouse s’intègre dans votre pile de données moderne. Inscrivez-vous maintenantSolutionsSolutions par secteurServices financiersSanté et sciences du vivantProduction industrielleCommunications, médias et divertissementSecteur publicVente au détailDécouvrez tous les secteurs d'activitéSolutions par cas d'utilisationSolution AcceleratorsServices professionnelsEntreprises digital-nativesMigration des plateformes de données9 mai | 8h PT   Découvrez le Lakehouse pour la fabrication Découvrez comment Corning prend des décisions critiques qui minimisent les inspections manuelles, réduisent les coûts d’expédition et augmentent la satisfaction des clients.Inscrivez-vous dès aujourd’huiApprendreDocumentationFORMATION ET CERTIFICATIONDémosRessourcesCommunauté en ligneUniversity AllianceÉvénementsSommet Data + IABlogLabosBeacons26-29 juin 2023 Assistez en personne ou connectez-vous pour le livestream du keynoteS'inscrireClientsPartenairesPartenaires cloudAWSAzureGoogle CloudContact partenairesPartenaires technologiques et de donnéesProgramme partenaires technologiquesProgramme Partenaire de donnéesBuilt on Databricks Partner ProgramPartenaires consulting et ISProgramme Partenaire C&SISolutions partenairesConnectez-vous en quelques clics à des solutions partenaires validées.En savoir plusEntrepriseOffres d'emploi chez DatabricksNotre équipeConseil d'administrationBlog de l'entreprisePresseDatabricks VenturesPrix et distinctionsNous contacterDécouvrez pourquoi Gartner a désigné Databricks comme leader pour la deuxième année consécutiveObtenir le rapportEssayer DatabricksRegarder les démosNous contacterLoginJUNE 26-29REGISTER NOWApprendrePlongez dans l'univers de Databricks et explorez les ressources qui s'offrent à vous. Vous y trouverez des formations, des certifications, des événements à venir, de la documentation utile et bien plus encore.Démarrer avec DatabricksApprenez les fondamentauxLa plateforme Lakehouse de Databricks permet de construire et d'exécuter facilement des pipelines de données, de collaborer à des projets de Data Science et d'analytique, tout en construisant et déployant des modèles de Machine Learning. Consultez nos guides de prise en main ci-dessous.Vous débutez avec Databricks ? Démarrez votre parcours Databricks avec les conseils d'un ingénieur de la réussite client expérimenté.Onboarding Databricks Lakehouse Fundamentals Training Watch 4 short videos, take the quiz and get your badge to share on LinkedIn or your résumé Get startedData Science et Data EngineeringAdministrateurs DatabricksAnalyses SQLMachine learningFormationOptez pour la formation de la Databricks Academy. Découvrez comment maîtriser l'analytique des données avec l'équipe à l'origine du projet de recherche Apache Spark™ à l'université de Californie à Berkeley.Databricks AcademyCertificationLes examens de certification évaluent votre maîtrise de la plateforme Databricks Lakehouse et des méthodes indispensables à la mise en œuvre de projets de qualité. Ils vous aident à obtenir la reconnaissance de votre secteur, à vous détacher de la concurrence et à améliorer votre productivité et vos résultats. Ils offrent aussi une preuve tangible de votre investissement dans la formation.Certification DatabricksObtenez des réponses à vos questionsDifférentes options s'offrent à vous pour obtenir des réponses aux questions que vous pourriez vous poser à vos débuts :Interrogez un expert technique Databricks en directCommunauté en ligneExplorez des sujets populaires au sein de la communauté Databricks.Interagissez avec la communautéSujets populaires du forumDatabricksDelta LakeSparkSQLpysparkAzureAWSGCPBesoin d'un coup de main ?Si vous bénéficiez d'un contrat d'assistance ou que vous souhaitez en contracter un, consultez nos options ci-dessous. Pour obtenir des conseils stratégiques (avec un ingénieur Customer success ou un contrat de Services professionnels), adressez-vous à l’administrateur de votre workspace pour contacter votre chargé de compte Databricks.Consultez nos options de soutienGalerie NotebookCette galerie présente certaines des possibilités offertes par les notebooks axés sur les technologies et les cas d'usage pouvant facilement être importés dans votre propre environnement Databricks, ou dans l'édition gratuite de la communauté.Galerie Notebook DatabricksDocumentationLe site dédié à la documentation technique de Databricks fournit des conseils pratiques et des informations de référence pour la Data Science, la Data Engineering et le Machine Learning de Databricks, ainsi que pour les environnements adaptés à différents profils de Databricks SQL.Documentation AWSDocumentation AzureDocumentation GoogleÉvénements et communauté DatabricksSommet Data + IARejoignez nos conférences, annonces de produits et plus de 200 sessions techniques, avec un éventail d'experts du secteur, du monde universitaire et de la recherche.En savoir plusÉvénements mondiauxRéservez votre place à l'une de nos conférences mondiales ou régionales, démonstrations de produits en direct, ou à l'un de nos webinars, événements parrainés par des partenaires ou meetups.En savoir plusMeetups en ligne sur les données et l'IADécouvrez ce qu'il se passe dans les groupes Databricks Meetup du monde entier et rejoignez-en un, virtuellement, près de chez vous.En savoir plusBeaconsDécouvrez les Databricks Beacons, un groupe de membres de la communauté qui font tout leur possible pour améliorer la communauté des données et de l'IA.En savoir plusUniversity AllianceRejoignez la Databricks University Alliance pour accéder à des ressources gratuites destinées aux éducateurs souhaitant enseigner à l'aide de Databricks.En savoir plusDiffusion vidéo Data BrewExploration des données + IA avec des expertsBrooke Wenig et Denny LeeSérie de vidéosConférences et meetups en ligne sur les technologiesBrooke Wenig et Denny LeeEntretiens techniquesGérer le cycle de vie du Machine Learning de bout en bout en utilisant MLflowJules DamjiEntretiens techniquesDémarrer avec Delta LakeDenny LeeEntretiens techniquesSe plonger dans Delta Lake (avancé)Denny LeeDatabricks Demo Hub (démonstrations de produits)Recherche sur les données et l'IAMatei ZahariaCofondateur et technologiste en chef deDatabricksReynold XinCofondateur et Chief ArchitectDatabricksSue Ann HongIngénieur logicielDatabricksLisez les articles récents des fondateurs, des collaborateurs et des chercheurs de Databricks sur les systèmes distribués, l'IA et l'analytique des données, en collaboration avec des universités de premier plan comme celle de Californie à Berkeley ou de Stanford.Accéder à la rechercheProduitPlatform OverviewTarifsOpen Source TechEssayer DatabricksDémoProduitPlatform OverviewTarifsOpen Source TechEssayer DatabricksDémoLearn & SupportDocumentationGlossaryFORMATION ET CERTIFICATIONHelp CenterLegalCommunauté en ligneLearn & SupportDocumentationGlossaryFORMATION ET CERTIFICATIONHelp CenterLegalCommunauté en ligneSolutionsBy IndustriesServices professionnelsSolutionsBy IndustriesServices professionnelsEntrepriseNous connaîtreOffres d'emploi chez DatabricksDiversité et inclusionBlog de l'entrepriseNous contacterEntrepriseNous connaîtreOffres d'emploi chez DatabricksDiversité et inclusionBlog de l'entrepriseNous contacterDécouvrez les offres d'emploi chez Databrickspays/régionsEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Avis de confidentialité|Conditions d'utilisation|Vos choix de confidentialité|Vos droits de confidentialité en Californie
https://www.databricks.com/dataaisummit/speaker/onik-kurktchian
Onik Kurktchian - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingOnik KurktchianHead of S&P Global Marketplace Workbench at S&P GLOBALBack to speakersOnik Kurktchian is a Product Manager and leads the Analytical Platforms and Services in S&P Global Market Intelligence. He is part of the team that is responsible for creating products and services that enable data exploration and workflow solutions across all client segments. Previously, he has worked as a Product Specialist across multiple S&P products between Desktop, Excel and Feed Solutions. Onik holds an MSc from LSE in Economic History and a BSc from Queen Mary in Economics & Finance. Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/customers/rivian
Customer Story: Rivian | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCUSTOMER STORYDriving into the future of electric transportation250 platform usersA 50x increase from a year ago INDUSTRY: Manufacturing SOLUTION: Predictive maintenance,scaling ML models for IoT,data-driven ESG PLATFORM: Lakehouse,Delta Lake,Unity Catalog CLOUD: AWS“Databricks Lakehouse empowers us to lower the barrier of entry for data access across our organization so we can build the most innovative and reliable electric vehicles in the world.” — Wassym Bensaid, Vice President of Software Development, RivianRivian is preserving the natural world for future generations with revolutionary Electric Adventure Vehicles (EAVs). With over 25,000 EAVs on the road generating multiple terabytes of IoT data per day, the company is using data insights and machine learning to improve vehicle health and performance. However, with legacy cloud tooling, it struggled to scale pipelines cost-effectively and spent significant resources on maintenance — slowing its ability to be truly data driven. Since moving to the Databricks Lakehouse Platform, Rivian can now understand how a vehicle is performing and how this impacts the driver using it. Equipped with these insights, Rivian is innovating faster, reducing costs, and ultimately, delivering a better driving experience to customers.Struggling to democratize data on a legacy platformBuilding a world that will continue to be enjoyed by future generations requires a shift in the way we operate. At the forefront of this movement is Rivian — an electric vehicle manufacturer focused on shifting our planet’s energy and transportation systems entirely away from fossil fuel. Today, Rivian’s fleet includes personal vehicles and involves a partnership with Amazon to deliver 100,000 commercial vans. Each vehicle uses IoT sensors and cameras to capture petabytes of data ranging from how the vehicle drives to how various parts function. With all this data at its fingertips, Rivian is using machine learning to improve the overall customer experience with predictive maintenance so that potential issues are addressed before they impact the driver.Before Rivian even shipped its first EAV, it was already up against data visibility and tooling limitations that decreased output, prevented collaboration and increased operational costs. It had 30 to 50 large and operationally complicated compute clusters at any given time, which was costly. Not only was the system difficult to manage, but the company experienced frequent cluster outages as well, forcing teams to dedicate more time to troubleshooting than to data analysis. Additionally, data silos created by disjointed systems slowed the sharing of data, which further contributed to productivity issues. Required data languages and specific expertise of toolsets created a barrier to entry that limited developers from making full use of the data available. Jason Shiverick, Principal Data Scientist at Rivian, said the biggest issue was the data access. “I wanted to open our data to a broader audience of less technical users so they could also leverage data more easily.”Rivian knew that once its EAVs hit the market, the amount of data ingested would explode. In order to deliver the reliability and performance it promised, Rivian needed an architecture that would not only democratize data access, but also provide a common platform to build innovative solutions that can help ensure a reliable and enjoyable driving experience.Predicting maintenance issues with Databricks LakehouseRivian chose to modernize its data infrastructure on the Databricks Lakehouse Platform, giving it the ability to unify all of its data into a common view for downstream analytics and machine learning. Now, unique data teams have a range of accessible tools to deliver actionable insights for different use cases, from predictive maintenance to smarter product development. Venkat Sivasubramanian, Senior Director of Big Data at Rivian, says, “We were able to build a culture around an open data platform that provided a system for really democratizing data and analysis in an efficient way.” Databricks’ flexible support of all programming languages and seamless integration with a variety of toolsets eliminated access roadblocks and unlocked new opportunities. Wassym Bensaid, Vice President of Software Development at Rivian, explains, “Today we have various teams, both technical and business, using Databricks Lakehouse to explore our data, build performant data pipelines, and extract actionable business and product insights via visual dashboards.”Rivian’s ADAS (advanced driver-assistance systems) Team can now easily prepare telemetric accelerometer data to understand all EAV motions. This core recording data includes information about pitch, roll, speed, suspension and airbag activity, to help Rivian understand vehicle performance, driving patterns and connected car system predictability. Based on these key performance metrics, Rivian can improve the accuracy of smart features and the control that drivers have over them. Designed to take the stress out of long drives and driving in heavy traffic, features like adaptive cruise control, lane change assist, automatic emergency driving, and forward collision warning can be honed over time to continuously optimize the driving experience for customers.Secure data sharing and collaboration was also facilitated with the Databricks Unity Catalog. Shiverick describes how unified governance for the lakehouse benefits Rivian productivity. “Unity Catalog gives us a truly centralized data catalog across all of our different teams,” he said. “Now we have proper access management and controls.” Venkat adds, "With Unity Catalog, we are centralizing data catalog and access management across various teams and workspaces, which has simplified governance.” End-to-end version controlled governance and auditability of sensitive data sources, like the ones used for autonomous driving systems, produces a simple but secure solution for feature engineering. This gives Rivian a competitive advantage in the race to capture the autonomous driving grid.Accelerating into an electrified and sustainable worldBy scaling its capacity to deliver valuable data insights with speed, efficiency and cost-effectiveness, Rivian is primed to leverage more data to improve operations and the performance of its vehicles to enhance the customer experience. Venkat says, “The flexibility that lakehouse offers saves us a lot of money from a cloud perspective, and that’s a huge win for us.” With Databricks Lakehouse providing a unified and open source approach to data and analytics, the Vehicle Reliability Team is able to better understand how people are using their vehicles, and that helps to inform the design of future generations of vehicles. By leveraging the Databricks Lakehouse Platform, they have seen a 30%–50% increase in runtime performance, which has led to faster insights and model performance.Shiverick explains, “From a reliability standpoint, we can make sure that components will withstand appropriate lifecycles. It can be as simple as making sure door handles are beefy enough to endure constant usage, or as complicated as predictive and preventative maintenance to eliminate the chance of failure in the field. Generally speaking, we’re improving software quality based on key vehicle metrics for a better customer experience.”From a design optimization perspective, Rivian’s unobstructed data view is also producing new diagnostic insights that can improve fleet health, safety, stability and security. Venkat says, “We can perform remote diagnostics to triage a problem quickly, or have a mobile service come in, or potentially send an OTA to fix the problem with the software. All of this needs so much visibility into the data, and that’s been possible with our partnership and integration on the platform itself.” With developers actively building vehicle software to improve issues along the way.Moving forward, Rivian is seeing rapid adoption of Databricks Lakehouse across different teams — increasing the number of platform users from 5 to 250 in only one year. This has unlocked new use cases including using machine learning to optimize battery efficiency in colder temperatures, increasing the accuracy of autonomous driving systems, and serving commercial depots with vehicle health dashboards for early and ongoing maintenance. As more EAVs ship, and its fleet of commercial vans expands, Rivian will continue to leverage the troves of data generated by its EAVs to deliver new innovations and driving experiences that revolutionize sustainable transportation.Ready to get started?Try Databricks for freeLearn more about our productTalk to an expertProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/company/partners/consulting-and-si/partner-solutions/cognizant-video-quality-experience
Cognizant Video Quality of Experience | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWBrickbuilder SolutionVideo Quality of Experience by CognizantIndustry-specific solution developed by Cognizant and powered by the Databricks Lakehouse Platform Get startedIdentify and remediate video quality of experience issuesWhether it’s a delayed time-to-first-frame, a playback failure or a rebuffing event, it only takes one poor video experience for viewers to churn. Cognizant’s Video Quality of Experience (QoE) solution limits the occurrence of such events by pairing fine-grained telemetry with ML and AI to identify and remediate video QoE issues in near real-time.Optimize the video viewing experience for your fansIncrease user engagementReduce churnGet startedDeliver AI innovation faster with solution accelerators for popular industry use cases. See our full library of solutions ➞ProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/dataaisummit/speaker/dillon-bostwick/#
Dillon Bostwick - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingDillon BostwickSolutions Architect at DatabricksBack to speakersDillon Bostwick has spent the last 5 years as a Solutions Architect at Databricks, where he has an extensive background in working with data engineers, data scientists, and business stakeholders to productionize data and machine learning projects. He is also active in developing new field projects intended to accelerate the management of data infrastructure.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/percy-liang
Percy Liang - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingPercy LiangProfessor of Computer Science at StanfordBack to speakersPercy Liang is an Associate Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/product/pricing/platform-addons
Databricks Platform and Add-OnsSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWPlatform Tiers and Add-OnsOverviewJobsDelta Live TablesDatabricks SQLData Science & MLModel ServingPlatform & Add-OnsCalculatorSelect cloudAWSAzureGoogle CloudPlatform TiersFeature DetailStandardPremiumEnterpriseDatabricks WorkspaceWorkspace for production jobs, analytics and MLWorkspace for production jobs, analytics and MLWorkspace for production jobs, analytics and MLPerformanceUp to 50x faster than Apache Spark™Optimized Runtime EngineGovernance and ManageabilityDatabricks Workspace administrationAudit logs and automated policy controlsAudit logs and automated policy controlsEnterprise SecuritySecured cloud and network architecture with authentications like single sign-onExtend your cloud-native security for company-wide adoptionAdvanced compliance and security for mission-critical dataPlatform Add-OnsStandardPremiumEnterpriseEnhanced Security and Compliance Provides enhanced security and controls for your compliance needs15% of Product SpendPay as you go with a 14-day free trial or contact us for committed-use discounts or custom requirements Calculate priceStart free trialContact usFAQHow is Product Spend calculated when used for add-on charges?Product Spend is calculated based on AWS product spend at list, before the application of any discounts, usage credits, add-on uplifts, or support fees.ProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/it/solutions/industries/manufacturing-industry-solutions
Soluzioni per l'industria manifatturiera - DatabricksSkip to main contentPiattaformaThe Databricks Lakehouse PlatformDelta LakeGovernance dei datiIngegneria dei datiStreaming di datiData warehouseCondivisione dei datiMachine LearningData SciencePrezziMarketplaceTecnologia open-sourceSecurity and Trust CenterWEBINAR 18 maggio / 8 AM PT Addio, Data Warehouse. Ciao, Lakehouse. Partecipa per capire come una data lakehouse si inserisce nel tuo stack di dati moderno. Registrati oraSoluzioniSoluzioni per settoreServizi finanziariSanità e bioscienzeIndustria manifatturieraComunicazioni, media e intrattenimentoSettore pubblicoretailVedi tutti i settoriSoluzioni per tipo di applicazioneAcceleratoriServizi professionaliAziende native digitaliMigrazione della piattaforma di dati9 maggio | 8am PT   Scopri la Lakehouse for Manufacturing Scopri come Corning sta prendendo decisioni critiche che riducono al minimo le ispezioni manuali, riducono i costi di spedizione e aumentano la soddisfazione dei clienti.Registrati oggi stessoFormazioneDocumentazioneFormazione e certificazioneDemoRisorseCommunity onlineUniversity AllianceEventiConvegno Dati + AIBlogLabsBeacons  26–29 giugno 2023 Partecipa di persona o sintonizzati per il live streaming del keynoteRegistratiClientiPartnerPartner cloudAWSAzureGoogle CloudPartner ConnectPartner per tecnologie e gestione dei datiProgramma Partner TecnologiciProgramma Data PartnerBuilt on Databricks Partner ProgramPartner di consulenza e SIProgramma partner consulenti e integratori (C&SI)Soluzioni dei partnerConnettiti con soluzioni validate dei nostri partner in pochi clic.RegistratiChi siamoLavorare in DatabricksIl nostro teamConsiglio direttivoBlog aziendaleSala stampaDatabricks VenturesPremi e riconoscimentiContattiScopri perché Gartner ha nominato Databricks fra le aziende leader per il secondo anno consecutivoRichiedi il reportProva DatabricksGuarda le demoContattiAccediJUNE 26-29REGISTER NOWScopri Lakehouse for ManufacturingRiduci i costi, aumenta la produttività e unifica il tuo ecosistema di dati sull'unica piattaforma pensata per il modo in cui le aziende manifatturiere usano i datiRegistratiContattiRiduci i costi di gestione (TCO). Migliora le prestazioni. Aumenta la scalabilità.Lakehouse for ManufacturingUnifica tutti i carichi di lavoro di dati, analisi e AI, con condivisione e governance integrate, in modo che i team interni ed esterni abbiano accesso ai dati di cui hanno bisogno, quando ne hanno bisogno.Impatto su tutta la catena del valoreCoinvolgimento del clienteRisultati precisi ed esperienze fluide per i clientiCon una vista a 360 gradi di clienti, attività operative e risorse, potrai ottenere il massimo in termini di disponibilità (uptime), qualità del servizio e valore economico lungo tutto il ciclo di vita del prodotto, con esiti personalizzati per ciascun cliente, servizi proattivi di assistenza sul campo e soluzioni mission-critical differenziate.Efficienza operativaProduttività dei dipendentiInnovazione di prodottoSoluzioni e partner per l'industria manifatturieraSoluzioni di analisi dei dati e AI senza compromessi realizzate appositamente per le aziende manifatturiereGli Acceleratori Databricks sono guide specifiche — notebook con funzionalità complete e best practice — che velocizzano il raggiungimento di risultati nel settore manifatturiero. Risparmia tempo nelle attività di scoperta, progettazione, sviluppo e collaudo per casi d'uso come gemelli digitali, efficienza delle apparecchiature (OEE), previsioni e altro ancora.Monitoraggio dell'efficacia totale dell'impianto (OEE) e dei KPIPrestazioni e scalabilità elevate nel monitoraggio di apparecchiature a 360 gradi Acquisisci ed elabora progressivamente dati da sensori/dispositivi IoT in diversi formati, elaborando ed estrapolando KPI e metriche per ottenere informazioni approfondite preziose.CominciaPrevisioni a livello di singola partePrevedere la domanda di singole parti per snellire la produzione Prevedi la domanda di parti a livello singolo invece che aggregato, per ridurre al minimo le interruzioni della supply chain e aumentare le vendite.CominciaGemelli digitaliAumentare l'efficienza operativa e migliorare i processi decisionali Elabora dati concreti in tempo reale, ricava informazioni approfondite su larga scala da trasferire a svariate applicazioni a valle, e ottimizza l'operatività degli impianti con decisioni guidate dai dati.CominciaScopri gli acceleratori per l'industria manifatturieraAbbiamo collaborato con le principali società di consulenza per fornire soluzioni innovative per settori specifici. Le soluzioni Databricks Brickbuilder aiutano a ridurre i costi e aumentare il valore generato dai dati. Con decenni di competenze settoriali alle spalle — e specificamente costruite per la Databricks Lakehouse Platform — le soluzioni Brickbuilder sono ritagliate su misura per le tue esigenze.Manifattura intelligente Sfrutta i dati, aumenta l'interoperabilità e fornisci informazioni avanzate su larga scala sfruttando analisi e AI.Maggiori informazioniIspezione di qualitàAutomatizza il controllo di qualità con la visione artificiale per individuare difetti, corpi estranei, anomalie o configurazioni errate.Maggiori informazioniGestione predittiva del rischio di fornituraAumenta all'ennesima potenza la visibilità sui flussi di ordini e sulle prestazioni dei fornitori, per massimizzare l'efficienza, gestire le eccezioni e migliorare la resilienza.Maggiori informazioniScopri tutte le soluzioni dei partner“Databricks Lakehouse ci consente di eliminare le barriere di accesso ai dati in tutta la nostra organizzazione, aiutandoci a realizzare i veicoli elettrici più innovativi e affidabili al mondo”. – Wassym Bensaid, Vice President of Software Development, Rivian “L'uso di Databricks nel corso degli anni è aumentato notevolmente. Abbiamo cominciato a usare Databricks come piattaforma di Big Data e AI e il suo impiego si è progressivamente ampliato. Abbiamo un gruppo completamente diverso di citizen engineer e data scientist che usano Databricks come strumento moderno di business intelligence per prendere decisioni più efficaci." —Daniel Jeavons, General Manager, Advanced Analytics CoE, Shell “La piattaforma Databricks ci ha aiutato a ridurre al minimo i rischi di indisponibilità del motore, ad accorciare i tempi di consegna dei ricambi e ad aumentare l'efficienza nelle rotazioni di magazzino: tutto questo ci consente di proporre TotalCare, il nostro programma di manutenzione Power-by-the-Hour (PBH) leader nel settore dell'aviazione”. — Stuart Hughes, Chief Information and Digital Officer, Rolls-Royce Civil Aerospace RisorseeBookMetti a frutto i dati dell'ERPWebinarMigliorare la manutenzione predittiva per le aziende manifatturiere con dati e AIeBookQuattro forze che guidano la manifattura intelligentePronto per cominciare?Vorremmo conoscere i tuoi obiettivi aziendali e come il nostro team di servizi potrebbe aiutarti a realizzarli.Prova Databricks gratisContattiProdottoPanoramica della piattaformaPrezziTecnologia open-sourceProva DatabricksDemoProdottoPanoramica della piattaformaPrezziTecnologia open-sourceProva DatabricksDemoFormazione e supportoDocumentazioneGlossaryFormazione e certificazioneHelp CenterLegaleCommunity onlineFormazione e supportoDocumentazioneGlossaryFormazione e certificazioneHelp CenterLegaleCommunity onlineSoluzioniPer settoreServizi professionaliSoluzioniPer settoreServizi professionaliChi siamoChi siamoLavorare in DatabricksDiversità e inclusioneBlog aziendaleContattiChi siamoChi siamoLavorare in DatabricksDiversità e inclusioneBlog aziendaleContattiPosizioni aperte in DatabricksMondoEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Informativa sulla privacy|Condizioni d'uso|Le vostre scelte sulla privacy|I vostri diritti di privacy in California
https://www.databricks.com/dataaisummit/speaker/amine-benhamza
Amine Benhamza - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingAmine BenhamzaLead Solutions Architect at DatabricksBack to speakersAmine is a Sr. Solutions Architect specializing in Cloud, Data & AI, Migrations, and Enterprise Architecture. He has a significant experience and skills in: - Designing Cloud-Native Enterprise Architecture for SMBs and Fortune 500 Companies - Building & Lead Cross-Functional SME teams - Collaborating with Marketing and GTM teams to evangelize cloud-native technology to customers & partnersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/fr/solutions
Accélérateurs de solutions Databricks – Cas d'usage DatabricksSkip to main contentPlateformeThe Databricks Lakehouse PlatformDelta LakeGouvernance des donnéesData EngineeringStreaming de donnéesEntreposage des donnéesPartage de donnéesMachine LearningData ScienceTarifsMarketplaceOpen source techCentre sécurité et confianceWEBINAIRE mai 18 / 8 AM PT Au revoir, entrepôt de données. Bonjour, Lakehouse. Assistez pour comprendre comment un data lakehouse s’intègre dans votre pile de données moderne. Inscrivez-vous maintenantSolutionsSolutions par secteurServices financiersSanté et sciences du vivantProduction industrielleCommunications, médias et divertissementSecteur publicVente au détailDécouvrez tous les secteurs d'activitéSolutions par cas d'utilisationSolution AcceleratorsServices professionnelsEntreprises digital-nativesMigration des plateformes de données9 mai | 8h PT   Découvrez le Lakehouse pour la fabrication Découvrez comment Corning prend des décisions critiques qui minimisent les inspections manuelles, réduisent les coûts d’expédition et augmentent la satisfaction des clients.Inscrivez-vous dès aujourd’huiApprendreDocumentationFORMATION ET CERTIFICATIONDémosRessourcesCommunauté en ligneUniversity AllianceÉvénementsSommet Data + IABlogLabosBeacons26-29 juin 2023 Assistez en personne ou connectez-vous pour le livestream du keynoteS'inscrireClientsPartenairesPartenaires cloudAWSAzureGoogle CloudContact partenairesPartenaires technologiques et de donnéesProgramme partenaires technologiquesProgramme Partenaire de donnéesBuilt on Databricks Partner ProgramPartenaires consulting et ISProgramme Partenaire C&SISolutions partenairesConnectez-vous en quelques clics à des solutions partenaires validées.En savoir plusEntrepriseOffres d'emploi chez DatabricksNotre équipeConseil d'administrationBlog de l'entreprisePresseDatabricks VenturesPrix et distinctionsNous contacterDécouvrez pourquoi Gartner a désigné Databricks comme leader pour la deuxième année consécutiveObtenir le rapportEssayer DatabricksRegarder les démosNous contacterLoginJUNE 26-29REGISTER NOWDatabricks pour l'industrieDes solutions d’analytique et d’IA sans compromis spécialement pensées pour votre secteur d’activitéDémarrerPlanifier une démoDécouvrez le Lakehouse dédié à votre secteur d'activitéCommunications, médias et divertissementSuscitez l'attention, captivez les imaginationsEn savoir plusServices financiersRenforcez la confiance, assurez la sérénitéEn savoir plusSanté et sciences du vivantDécouvrez et offrez des soins de meilleure qualitéEn savoir plusVente au détail et biens de consommationGuidez vos clients dans leur parcours et affirmez votre marqueEn savoir plusTous les secteurs d'activitésearchHide filtersIndustry🤔No results available. Try adjusting the filters or start a new search.reset the listSolutions pour l'industrieDe l’idée à la preuve de concept (PoC) en deux semaines seulementLes accélérateurs de solutions Databricks sont des guides spécialisés conçus pour accélérer les résultats à l'aide de notebooks entièrement fonctionnels et de bonnes pratiques. Les clients Databricks gagnent du temps dans leurs opérations de découverte, de conception, de développement et de test. Beaucoup d'entre eux passent de l'idée à la preuve de concept (PoC) en deux semaines seulement.Explorez les AccélérateursPrêt à commencer?Découvrez ce que Databricks peut faire pour vous avec un essai gratuit.Démarrez votre essai gratuitProduitPlatform OverviewTarifsOpen Source TechEssayer DatabricksDémoProduitPlatform OverviewTarifsOpen Source TechEssayer DatabricksDémoLearn & SupportDocumentationGlossaryFORMATION ET CERTIFICATIONHelp CenterLegalCommunauté en ligneLearn & SupportDocumentationGlossaryFORMATION ET CERTIFICATIONHelp CenterLegalCommunauté en ligneSolutionsBy IndustriesServices professionnelsSolutionsBy IndustriesServices professionnelsEntrepriseNous connaîtreOffres d'emploi chez DatabricksDiversité et inclusionBlog de l'entrepriseNous contacterEntrepriseNous connaîtreOffres d'emploi chez DatabricksDiversité et inclusionBlog de l'entrepriseNous contacterDécouvrez les offres d'emploi chez Databrickspays/régionsEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Avis de confidentialité|Conditions d'utilisation|Vos choix de confidentialité|Vos droits de confidentialité en Californie
https://www.databricks.com/company/careers/open-positions?department=finance
Current job openings at Databricks | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOW OverviewCultureBenefitsDiversityStudents & new gradsCurrent job openings at DatabricksDepartmentLocationProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/customers/wejo
Customer Story: wejo – DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCUSTOMER STORYEnabling the connected car with AI50xFaster time-to-insight due to Improved IT operations20xFaster data processing of vehicle and road data90%Decrease in time-to-market of new innovationsWatch video INDUSTRY: Automotive SOLUTION: Scaling ML models for IoT PLATFORM USE CASE: Delta Lake,data science,machine learning,ETL CLOUD: AWS"Before Databricks, the time to market would’ve been weeks, if not months to meet the analysis requirements for some of our customers. Now, it takes hours.” – Steve Pimblett, Chief Information Officer and Data Officer, wejowejo was founded with the global ambition to be the world’s largest connected car company. To date, wejo has curated over 140 billion miles of data and expects to have 17 million cars on its platform by the end of the year. With more than 15 billion data points every day and counting, wejo trusts Databricks to deliver ML-powered innovations to the automotive industry, and deliver better driving experiences.Pipelines stretched to ingest 3 trillion monthly data pointsIn order to drive value to the customer, wejo ingests streaming data across 50 million connected vehicles, processing data from OEMs and satellite navigation systems every three seconds. This data provides insights into improving traffic flow, reducing accidents, safety alerts and emergency services, right through to new innovations in optimizing parking. With various data streams coming in from multiple disjointed sources, harnessing the insights from the data through data science is very difficult and resource-intensive.Massive data volumes: They are processing over three trillion data points per month, all in a streaming environment from car to marketplace in less than 40 seconds — requires significant scale within a low latency environment.Challenges to scale: With so much data to ingest, wejo struggled with relying on Mapreduce clusters which were rigid in size and limited in the libraries that were available. This would result in days of delay waiting for the right Python modules to be installed, which slowed innovation.Slow performance: Long-running jobs could take hours if not days to process.Reliable, performant data pipelines at scale with Delta LakeDatabricks provides wejo with a unified data analytics platform that has fostered a scalable and collaborative environment across data science and engineering, allowing data teams to more quickly innovate and deliver ML-powered innovations to the automotive industry.Managed platform in the cloud simplifies the provisioning of compute clusters to any size.Support for multiple languages (SQL, Scala, Python, R) improves collaboration across data engineering, data science, and analysts.Native support for Delta Lake allows their data engineering team to reliably run and scale both batch and streaming pipelines on the same data.Making the roads safer with ML innovationsWith Databricks, wejo is now able to do large-scale data processing and machine learning faster and cheaper. But most importantly, they are now able to easily share the output across the team and the organization — enabling others to drive innovation into the market.Improved operational efficiency: Features such as auto-scaling clusters has improved data engineering operations, accelerating pipelines for downstream analytics from weeks to minutes.Better cross-team collaboration: Shared notebook environment with support for multiple languages has improved team productivity.Faster time-to-insight: We now get in over a 20x performance benefit over open-source Spark with Databricks and 90% decrease in time to market. Ready to get started?Try Databricks for freeLearn more about our productTalk to an expertProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/dataaisummit/speaker/ryan-boyd/#
Ryan Boyd - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingRyan BoydCo-founder at MotherDuckBack to speakersRyan Boyd (LinkedIn) is a Boulder-based software engineer, data + authNZ geek and technology executive. He's currently a co-founder at MotherDuck, where they're making data analytics fun, frictionless and ducking awesome. He previously led developer relations teams at Databricks, Neo4j and Google Cloud. He's the author of O'Reilly's Getting Started with OAuth 2.0.Ryan advises B2B SaaS startups on growth marketing and developer relations as a Partner at Hypergrowth Partners. Prior to leading the Google Cloud Developer Relations team, he spent 7 years at Google working on 20+ different developer products and was the co-founder of Google Code Labs which aimed to improve quality and stability of Google's developer products.Ryan graduated with a degree in Computer Science from Rochester Institute of Technology (RIT) where he later worked full-time building web applications + APIs and architecting the central web hosting platform.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/sneh-kakileti
Sneh Kakileti - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingSneh KakiletiVice President, Product Management at ZoomInfoBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/dataaisummit/speaker/anfisa-kaydak/#
Anfisa Kaydak - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingAnfisa KaydakVP, Data Product & Engineering at HealthverityBack to speakersAnfisa studies applied math in State University, Minsk, Belarus. She started her career in the US as a Web developer and quickly became fascinated with data. The journey from gigabytes quickly progressed to petabytes, from RDMS to distributed systems, from data exploration to complex APLD studies and pipeline engineering. She is SME in healthcare data and analytics, and adept in Data and AI technology transformations in healthcare.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/blog/2022/03/09/introducing-lakehouse-for-healthcare-and-life-sciences.html
Introducing Lakehouse for Healthcare and Life Sciences - The Databricks BlogSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCategoriesAll blog postsCompanyCultureCustomersEventsNewsPlatformAnnouncementsPartnersProductSolutionsSecurity and TrustEngineeringData Science and MLOpen SourceSolutions AcceleratorsData EngineeringTutorialsData StreamingData WarehousingData StrategyBest PracticesData LeaderInsightsIndustriesFinancial ServicesHealth and Life SciencesMedia and EntertainmentRetailManufacturingPublic SectorIntroducing Lakehouse for Healthcare and Life SciencesDelivering better patient outcomes with data and AIby Michael Sanky and Michael OrtegaMarch 9, 2022 in NewsShare this postEach of us will likely generate millions of gigabytes of health data in our lifetimes: medical and pharmacy claims, electronic medical records with extensive clinical documentation, medical images; perhaps streaming data from wearable devices, blood biopsy data that can detect cancer, and genomic sequencing. These data sets have enormous potential to uncover new, life-saving treatments, predict disease before it happens, and fundamentally change the way that care is delivered. For healthcare and life sciences organizations seeking to deliver better patient outcomes, legacy technology is most often the rate-limiting factor. Common challenges include: Data silos and limited support for semi-structured data (like provider notes) and unstructured data (like images) prevent organizations from gaining a holistic view of a patientRapid growth in data is outpacing the scale of existing infrastructure, preventing population-level researchBatch processing and disjointed analytic tools prevent real-time response to challenges such as supply chain constraints and ICU bed capacityTraditional data architectures that don’t support advanced analytics and AI use casesSadly, for these reasons, the opportunity to tap into AI-driven innovation is simply out of reach for most organizations on the front lines of developing new drugs and treating patients in need. Meet Lakehouse for Healthcare and Life SciencesWell, that’s changing! Today, we’re thrilled to introduce the Lakehouse for Healthcare and Life Sciences — a platform designed to help organizations collaborate with data and AI in service of a unified goal: improving health outcomes. The Lakehouse eliminates the need for legacy data architectures, which have inhibited innovation, by providing a simple, open and multi-cloud platform for all your data, analytics and AI workloads. Building on this foundation are solution accelerators developed by Databricks and our ecosystem of partners for high-value analytics and AI use cases such as disease prediction, medical image classification, and biomarker discovery. To play this video, click here and accept cookiesWe know that healthcare organizations face a unique, and often painful, set of challenges that can significantly hinder innovation. We’ve designed the Lakehouse to address these challenges and provide the following benefits: Build a 360 degree view of the patient: It’s widely accepted that a vast majority of medical data is unstructured, which makes gaining a holistic patient view that much harder with siloed systems. This problem gets exponential as healthcare becomes increasingly interconnected between healthcare providers, payers and pharma manufacturers. Lakehouse is open by design and supports all data types, enabling organizations to create a 360 degree view of patient health. Couple this with the pre-built data ingestion and curation solution accelerators to bring health data to your lakehouse, and it’s even easier.Scale analytics for population-level insights: Scale is critical for initiatives like population health analytics and drug discovery, but for years legacy technology has failed to keep up with ballooning health data like genomics and imaging. Built in the cloud and designed for performance, the Lakehouse supports the largest of data jobs at lightning-fast speeds. For example, Regeneron reduced data processing from 3 weeks to 5 hours, and genotype- phenotype queries from 30 minutes to 3 seconds for workloads that scaled to 1.5M exomes. With the Lakeouse, organizations can quickly and reliably analyze data for millions of patients.Deliver real-time care and operations: Healthcare happens in real-time and requires real-time insights for critical use cases from managing ICU capacity to monitoring the distribution of temperature-sensitive vaccines. Unfortunately, traditional data warehouses aren’t designed to operate in real-time. The Lakehouse enables real-time analysis on streaming data so organizations can deliver care when it's needed, not after the fact.Leverage predictive health insights: The future of healthcare is predictive, not descriptive. The Lakehouse provides a robust set of analytics and AI tools directly connected to your data so organizations can innovate drug discovery and patient care with machine learning. Additionally, our network of partners has built accelerators for high-value analytics and AI use cases, including drug targeting and repurposing, drug safety monitoring, disease prediction and digital pathology analysis for cancer detection.With these capabilities, Databricks is empowering a new breed of data and AI innovators in healthcare: Using AI to develop diagnostic and therapeutic products that help children living with behavioral conditionsApplied machine learning to 17M+ electronic health records to identify new treatment indications for approved therapies.Delivering recommendations to patients using streaming data from connected health wearables for diabetes management.Tailor-made Solutions for Healthcare & Life SciencesTo help organizations realize value from their Lakehouse projects faster, Databricks and our ecosystem of partners have developed solution accelerators and open-source libraries—like Glow for genomics and Smolder for HL7v2 messages—to address common industry use cases. Data Ingestion and Curation Tools - easily ingest structured and unstructured health data (e.g. FHIR/HL7v2, imaging, genomics) into your Lakehouse for analytics at scale with our templates for data ingestion and curation.Analytics and AI Templates - packaged solutions for high-value analytics and AI use cases such as drug target identification, drug repurposing, disease risk prediction, medical image analytics (e.g. detecting cancer in pathology images) and more.Featured partner solutionsIntelligent Drug RepurposingInteroperabilityNatural Language Processing for HealthcareBiomedical Research Intelligent Data ManagementIdentify new therapeutic uses for existing drugs with the power of data and machine learning.Automate the ingestion of streaming FHIR bundles into your lakehouse and standardize with OMOP for patient analytics at scale.Extract insights from unstructured medical text for use cases such as automated PHI removal, adverse event detection, and oncology research.Improve biomarker discovery for precision medicine with a highly scalable and extensible whole-genome processing solution.Check out our full set of solutions on our Lakehouse for Healthcare and Life Sciences page. Get started building your LakehouseYou have the data. Now you have the platform. Join the hundreds of healthcare and life sciences organizations innovating on the Lakehouse. Here are some resources to help you get started: Join us at HIMSS to meet with our technical experts and participate in live use case demosCheck out our new ebook Improving Health Outcomes with Data + AITry Databricks for freeGet StartedSee all News postsProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/glossary/what-are-ml-pipelines
What are ML Pipelines?PlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWML PipelinesAll>ML PipelinesTypically when running machine learning algorithms, it involves a sequence of tasks including pre-processing, feature extraction, model fitting, and validation stages. For example, when classifying text documents might involve text segmentation and cleaning, extracting features, and training a classification model with cross-validation. Though there are many libraries we can use for each stage, connecting the dots is not as easy as it may look, especially with large-scale datasets. Most ML libraries are not designed for distributed computation or they do not provide native support for pipeline creation and tuning.The ML Pipelines is a High-Level API for MLlib that lives under the "spark.ml" package. A pipeline consists of a sequence of stages. There are two basic types of pipeline stages: Transformer and Estimator. A Transformer takes a dataset as input and produces an augmented dataset as output. E.g., a tokenizer is a Transformer that transforms a dataset with text into an dataset with tokenized words. An Estimator must be first fit on the input dataset to produce a model, which is a Transformer that transforms the input dataset. E.g., logistic regression is an Estimator that trains on a dataset with labels and features and produces a logistic regression model.Additional ResourcesManaged MLflow ProductMemory Optimization and Reliable Metrics in ML Pipelines at NetflixProductionizing Spark ML Pipelines with the Portable Format for AnalyticsPractical ML | Virtual EventFree Training: Building and Deploying Machine Learning ModelsBack to GlossaryProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/dataaisummit/speaker/taylor-hosbach/#
Taylor Hosbach - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingTaylor HosbachData Scientist at DIRECTVBack to speakersTaylor Hosbach is a Data Scientist at DIRECTV. He joins DIRECTV from Hopper and Epic where he utilized machine learning and advanced analytics to deliver actionable insights that improve business operations. He also has experience managing teams and working cross-functionally with the business to drive impact and opportunities for implementing Data Science solutions. Taylor holds an M.S. in Data Science from The University of Texas at Austin and a B.S. from Lehigh University.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/blog/2021/01/29/strategies-for-modernizing-investment-data-platforms.html
Strategies for Modernizing Investment Data Platforms - The Databricks BlogSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCategoriesAll blog postsCompanyCultureCustomersEventsNewsPlatformAnnouncementsPartnersProductSolutionsSecurity and TrustEngineeringData Science and MLOpen SourceSolutions AcceleratorsData EngineeringTutorialsData StreamingData WarehousingData StrategyBest PracticesData LeaderInsightsIndustriesFinancial ServicesHealth and Life SciencesMedia and EntertainmentRetailManufacturingPublic SectorStrategies for Modernizing Investment Data Platformsby Ricardo PortillaJanuary 29, 2021 in Engineering BlogShare this postThe appetite for investment was at a historic high in 2020 for both individual and institutional investors. One study showed that “retail traders make up nearly 25% of the stock market following COVID-driven volatility”. Moreover, institutional investors have piled on investments in cryptocurrency, with 36% invested in cryptocurrency, as outlined in Business Insider . As investors gain access to and trade alternative assets such as cryptocurrency, trading volumes have skyrocketed and created new data challenges. Moreover, cutting edge research is no longer restricted to institutional investors on Wall Street -- today’s world of investing extends to digital exchanges in Silicon Valley, data-centric market makers, and retail brokers that are investing increasingly in AI-powered tools for investors. Data lakes have become standard for building financial data products and research, but they come with a unique set of challenges: Lack of blueprints for how to build an enterprise data lake in the cloudOrganizations are still struggling to guarantee both reliability and timeliness of their data, leading to sub-optimal processes and diluted insightsAs a result, scalable AI (such as volatility forecasting) is difficult to achieve due to high maintenance costs and the lack of a blueprint for scale and hence trading profitability. As part of our suggested blueprint, we recommend standardization on Delta Lake, which is an open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads. A table in Delta Lake is both a batch table, as well as a streaming source and sink. Streaming data ingest, batch historic backfill, and interactive queries all just work out of the box. In particular, since raw market data is delivered in real-time and must be used in near real-time to support trading decisions, Delta Lake is critical to support trading use cases. This blog has 2 main sections. The first covers detailed options for landing financial market data into Delta Lake. The second section covers a blueprint for productionalizing use cases such as financial product volatility forecasting as well as market surveillance on Delta Lake. Notably, as part of the use cases, we introduce an open-source time-series package developed as part of Databricks Labs, which helps build the foundation for the use cases above. How to build a market Delta LakeIn this blog, through a series of design patterns and real-world examples, we will address the data challenges from the previous section. As a visual guide, the reference architecture below will be the basis for how to build out data lake sources and curate datasets for end reporting, trading summaries, and market surveillance alerts. Fundamental data source ingestionFundamental data, (positioned at the top left in Figure 1) loosely defined as economic and financial factors used to measure a company’s intrinsic value, is available today from most financial data vendors. Two of the most common sources include Factset and S&P Market Intelligence Platform. Both of these sources make data available via FTP, API, and a SQL Server database. Since data is available via a database for factor analysis, there are three easy options for ingestion into Delta Lake: Option 1 - Partner ingestion networkDatabricks has partnered with six companies which make up the “Data Ingestion Network of Partners.” Our partners have capabilities to ingest data from a variety of sources, including FTP, CRMs, marketing sources, and database sources. Since financial vendors allow financial clients to host databases, our partner tools can be used to pull out data to store directly in Delta Lake. Full documentation on how to ingest using this network and a listing of partners is located at Databricks’ documentation, Partner data integrations. Option 2 - Use native cloud-based ingestion toolsCloud service providers also have existing tools for database replication into Delta Lake. Below are two options for ingesting from databases (on-prem or cloud) into Delta Lake. AWSAWS offers a solution, Database Migration Services, which allows organizations to set up a Change Data Capture (CDC) process to replicate database changes to cloud data lakes. We  outlined a specific method for replicating database changes to Delta Lake in our blog, “Migrating Transactional Data to a Delta Lake using AWS DMS.” Since Xpressfeed S&P data, for example, has hundreds of sources, ranging from ESG risk scores and alternative data to fundamental earnings and news sentiment datasets, an automated way to replicate these to Delta Lake is critical. The AWS solution mentioned above provides a simple way to set this up. AzureOne of Azure’s most popular services is Azure Data Factory (ADF) and with good reason. ADF allows copying from many different data sources, including databases, FTP and even cross-cloud sources such as BigQuery. In particular, there are two methods of writing data to Delta Lake from a SQL database: ADF offers a simple ‘Copy To’ factory that simply copies database tables to blob storage (Blob or ADLS Gen2), and Delta Lake is a valid target table for this copy functionality.For a more customized transformation from a database to Delta Lake, ADF is flexible enough to read all tables from a database using an information schema as shown here. From here, one can simply configure a Databricks notebook which uses the input table name from the information schema and copies each table by executing a Databricks notebook which reads from the database using JDBC. Examples are here.API-based data source ingestionBloomberg is one of the industry standards for market data, reference data, and hundreds of other feeds. In order to show an example of API-based ingestion (middle left in Figure 1) from a Bloomberg data subscription, the B-PIPE (Bloomberg data API for accessing market data sources) emulator will be used. The Java market data subscription client code in the original emulator has been modified in the code below to publish events into a Kinesis real-time stream using the AWS SDK. Write B-PIPE market data to streaming service// Open Market Data Subscription service session.openServiceAsync("//blp/mktdata", new CorrelationID(-9999)); // Create list of securities to ingest continuously into Delta Lake SubscriptionList slist = new SubscriptionList(); slist.add(new Subscription("SPY US EQUITY", RunMarketDataSubscription._fields)); slist.add(new Subscription("AAPL 150117C00600000 EQUITY", RunMarketDataSubscription._fields)); slist.add(new Subscription("AMD US EQUITY", RunMarketDataSubscription._fields)); session.subscribe(slist) // Inside loop through continuous stream of messages from B-PIPE market data subscription // Use Kinesis client to write records retrieved from API to Kinesis stream AmazonKinesisClient kinesisClient = new AmazonKinesisClient(new BasicAWSCredentials(, )); String kinesisEndpointUrl = "https://kinesis.us-east-1.amazonaws.com"; String regionName = "us-east-1"; kinesisClient.setEndpoint(kinesisEndpointUrl); // Create PutRecordRegust with bytes from API request (output) and include sequence number PutRecordRequest putRecordRequest = new PutRecordRequest(); putRecordRequest.setStreamName( "databricks-bpipe" ); putRecordRequest.setData(ByteBuffer.wrap( output.getBytes() )); putRecordRequest.setPartitionKey( "mkt-data-partitionKey" ); putRecordRequest.setSequenceNumberForOrdering( sequenceNumberOfPreviousRecord ); PutRecordResult putRecordResult = kinesisClient.putRecord( putRecordRequest ); sequenceNumberOfPreviousRecord = putRecordResult.getSequenceNumber();Write data from Kinesis stream to Delta Lakeval kinesis = spark.readStream .format("kinesis") .option("streamName", "databricks-bpipe") .option("region", "us-east-1") .option("initialPosition", "latest") .load() val df = kinesis .withColumn("mktdata", col("data").cast("string")) .withColumn("event_ts", split(col("mktdata"), ",")(0)) .withColumn("ticker", split(split(col("mktdata"), ",")(1), " ")(1)) .withColumn("quote_pr", translate(split(col("mktdata"), ",")(2), "$", "")) .withColumn("event_dt", col("event_ts").cast("timestamp").cast("date")) df .writeStream .partitionBy("event_dt") .format("delta") .option("path", "/tmp/databricks/bpipe") .option("checkpointLocation", "/tmp/databricks/bpipe_cp") .start()Transform and read recordsdisplay(spark.read.format("delta").load("/tmp/databricks/bpipe")) Tick data source ingestionTick data (positioned in bottom left of Figure 1), which is the general term for high resolution intraday market data, typically comes from data vendors as batch sources in CSV, JSON or binary formats. Types of tick data include trade, quote, and contracts data, and an example of delivery  is the tick data history service offered by Thomson Reuters. The easiest way to continuously land data into Delta Lake from these sources is to set up the Databricks autoloader to read from a bucket and redirect data into a separate Delta Lake table. From here, various ETL processes might curate each message type into refined or aggregated Delta tables. The benefits of autoloader are twofold: Reliability and Performance inherited from Delta LakeLower costs due to underlying use of SQS (AWS ) or AQS (Azure) to avoid re-listing input files as well as a managed checkpoint to avoid manual selection of the most current unread files.From Delta Lake to financial services use case productionizationBeyond the data collection challenges that surface when building any data platform, investment management firms increasingly need to address the incorporation of AI into product suites, as well as managing costs for feature engineering. In particular: Both retail and institutional investment firms need to also be able to query and run ETL on data lakes in a cost-effective manner and minimize the amount of maintenance costs associated with enriching and querying data lakes.Retail investors expect AI-powered offerings and insights in subscriptions. The optimal solution will host the AI infrastructure in such a way that users can create AI-powered applications and dashboards where time spent on the setup of libraries and elastic compute infrastructure is minimized, and the underlying processing can be scaled to billions of data points that come in daily from transactional data sources (customer transactions and tick quotes alike).Now that we’ve presented reliable, efficient approaches for landing financial datasets into a cloud data lake, we want to address some of the existing gaps between financial datasets in the cloud and AI-powered products. The image above shows how datasets and siloed infrastructure are not enough to deliver investment analysis products in production. Most FSIs have adopted nearly all of the AI use case enablers on the right-hand side but have failed to maximize the volume-weighted overlap of these with core datasets. The Databricks Unified Data Analytics Platform subsumes the first four AI use case enablers out of the box. To make productionization more concrete, we’ll show how to use a new Databricks open-sourced package tempo for manipulating time series at scale. Then we’ll dive into the following use case feature creation templates which use tempo and show how to get the best of both worlds in the Venn diagram above. Retail investing details using fundamental data to inform daily volatility predictions.Market surveillance details a process for summarizing price improvement and detecting spoofing.Tempo - Time Series PackageIn financial services, time series are ubiquitous, and we find that our customers struggle with manipulating time series at scale. In the past, we have outlined a few approaches to scaling time-series queries. Now, Databricks Labs has released a simple common set of time-series utilities to make time-series processing simpler in an open-source package called tempo. This package contains utilities to do the following: AS OF joins to merge up to millions of irregular time series togetherFeature creation with rolling aggregations of existing metricsOptimized writes to Delta Lake ideal for ad-hoc time-series queriesVolume-weighted average price (VWAP) calculationsResamplingExponential Moving Average calculationsBy combining the versatile nature of tick data, reliable data pipelines and open source software like tempo, organizations can unlock exponential value from a variety of use cases at minimal costs and fast execution cycles. The next section walks through two recurring themes in capital markets which utilize tempo: volatility forecasting and market surveillance. Volatility forecasting methodology with fundamental and technical dataS&P Global Market Intelligence provides fundamental data that can be ingested using a mechanism called Xpressfeed (covered earlier in this guide). Some important points about this feed are that: It covers thousands of fundamental data metricsIt covers hundreds of thousands of globally listed and unlisted equitiesReporting frequency is daily – there is a filing date that can be used for point-in-time analysesAlthough we do not cover the curation process for the tick ETL (contact Databricks sales for more information on this use case), we outline the processing from standard tick formats to a final forecasting object using the tempo library; our implementation is in the links reported in the bottom of this blog. The high-level details are as follows: Create Point-in-time Calendar - Merge the latest fundamental data onto the latest calendar date using the filing date (fundamental data point filing date as of trade date). Commonly referred to as AS-OF join, this operation is usually expensive and subject to technical bottlenecks in a highly imbalanced dataset. tempo will guarantee this operation to be evenly distributed to leverage at best the cloud elasticity (and its associated costs).Create peer groups - using meaningful fundamental data items such as EPS, return on equity, float % (to represent stakeholder holdings), form peer groups based on each metric. Note that the data item values need to be pivoted to perform meaningful feature engineering here.Resample tick data to the hour (or whatever granularity desired). Hourly is chosen due to the fact that daily aggregation does not provide enough granularity for a good forecast on volatility.Forecast market volatility on Databricks using the runtime for machine learning.Aggregate forecasting results to find max / min volatility companies based on securities being evaluated. One of the noteworthy aspects of this data architecture is the last transitions when creating gold forecasting tables. In particular, We have incorporated ML as part of the feature engineering processing. This means we should apply full rigor for CI/CD as part of ML governance. Here is a template for accomplishing this in full rigor.We have chosen to highlight the importance of GPUs for forecasting volatility. In the notebook example at the end of this blog, we have chosen to use xgboost and simple range statistics on various quote metrics as part of our features. By leveraging the gpu_hist tree method and fully-managed GPU clusters and runtime, we can save 2.65X on costs (and 2.27X on runtime), both exhibiting the hard cost reduction and productivity savings for data teams.  These metrics were obtained on 6 months of tick data from a major US exchange.Ultimately, with the help of tempo and Databricks Runtime for Machine Learning, retail brokerages can service their clients with dashboards unifying fundamental and technical analysis using AI techniques. Below is the result of our peer group forecasts. Market surveillance methodology with tick dataMarket surveillance is an important part of the financial services ecosystem, which aims to reduce market manipulation, increase transparency, and enforce baseline rules for various assets classes. Some examples of organizations, both governmental and private, that have broad surveillance programs in place include NASDAQ, FINRA, CFTC, and CME Group. As the retail investing industry gets larger with newer and inexperienced investors (source), especially in the digital currency space, it is important to understand how to build a basic surveillance program that reduces financial fraud and increases transparency in areas such as market volatility, risk, and best execution. In the section below, we show how to build basic price improvement summaries, as well as putting a basic spoofing implementation together. Price improvementPrice improvement refers to the amount of improvement on the bid (in the case of a sell order) or the ask (in the case of a buy order) that brokers provide clients. This is important for a retail broker because it often contributes to perceived quality of a broker if it consistently saves clients money on a set of trades over time. The basic concept of price improvement is: Maria places a market order at 10:00 AM for stock XYZ for 100 shares at which the best bid/ask is $10/$11Broker A routes the order to an exchange to get an execution price of $10.95 per shareThe savings is $0.05 * 100 = $5.00 on this execution, representing some modest price improvementEven though the improvement is small, over time, these savings can add up over hundreds of trades. Some brokers display this information in-app also for transparency and to showcase the ability to route to appropriate market centers or market makers to get good prices. Calculating price improvementPrice improvement is really a special case of slippage (how much the execution price shifts from the best bid/ask at order arrival time). It affects digital currency as much as traditional equities, arguably more so since there is a high amount of volatility and order-volume fluctuation. For example, here are some insights on finance market depth and slippage. Below is a basic blueprint for how to calculate slippage using tempo (detailed code is available in the attached notebook): Ingest market order messages (orders placed)Ingest execution messagesPerform AS OF join to order arrival time using tempoPerform AS OF join to execution time using tempoMeasure the difference in the execution price and the bid/ask available at order arrival timeSummarize by firm and serve up in SQL analytics and/or BI dashboardsIngestion of order book data to get orders and executions is typically available in JSON or other flat file formats from internal systems or OMS (order management systems). Once this data is available, the AS OF join operates on a pair of data frames as described in the official tempo documentation here: Below we display the code which performs the join. from tempo.tsdf import TSDF trades = spark.table("exchange_trades") trades_tsdf = TSDF(trades, ts_col = 'event_ts', partition_cols = ["DATE", "TICKER"]) quotes_tsdf = TSDF(spark.table("tempo.delta_tick_quotes_6mo"), ts_col='event_ts', partition_cols = ["DATE", "TICKER"]) ex_asof = trades_tsdf.asofJoin(quotes_tsdf, right_prefix = "asof_ex_time") orders_tsdf = TSDF(ex_asof.df, ts_col = 'order_arrival_ts', partition_cols = ["DATE", "TICKER"]) order_asof = ex_asof.asofJoin(quotes_tsdf, right_prefix = "asof_ord_time") order_asof \ .df \ .write \ .format("delta") \ .mode('overwrite') \ .saveAsTable("tempo.silver_trade_slippage") Once this data is available in Delta Lake, it can be sliced in various ways to get a summary of those securities that have prominent slippage. See the example below, which summarizes the log of the aggregate slippage for a slice of time on a trading day. SpoofingSpoofing refers to a market manipulation pattern which involves entry of artificial interest (via fake order placement) followed by execution on the opposite side to take advantage of best bid/ask changes, which were falsely influenced by the original artificial interest. The spoofing order of events typically involves cancellation of orders as well - we outline a simple example below. Spoofing is one of the hundreds of different market manipulation techniques and occurs in many different asset classes. In particular, it has been part of most market surveillance programs for equities, but due to increased demand in digital currencies such as bitcoin and Ether, it is of increased importance. In fact, since the volatility of cryptocurrencies is so variable, it is critical to protect clients from potential spoofing activities to secure trust in crypto platforms, whether they be exchanges or DeFi frameworks. Sample patternThe sequence of steps to detect spoofing applies to other manipulation patterns (e.g. front-running, layering, etc), so we’ve outlined a simple approach to highlight some underlying techniques. Save order placement information - key on ORDER ID and sequence numberSave cancellation information for all orders (comes equipped with ORDER ID)Record NBBO at order arrival time (order_rcvd_ts in data below) as well as the NBBO prior to order arrivalJoin orders and cancellations (look for full cancellations) and record sequences of the following form: NBBO change at limit order placement from seconds prior to order placement (for a sell order, decrease in best ask)Cancellation at order placement (we refer to a fake order as a non-bonafide order)Execution on the opposite side of the order placement aboveWash trade (self-trade) activity by the same market participant (or as a nuance, this could represent different MPIDs under the same CRD)Sample pattern for capturing the NBBO (quote as a proxy here) information using the tempo AS OF join: from tempo.tsdf import TSDF orders_and_cncls_tsdf = TSDF(orders_and_cncls, ts_col = 'prior_order_rcvd_ts', partition_cols = ["DATE", "TICKER"]) prior_quotes_tsdf = TSDF(prior_quotes, ts_col='event_ts', partition_cols = ["DATE", "TICKER"]) prior_order_asof = orders_and_cncls_tsdf.asofJoin(prior_quotes_tsdf, right_prefix = "asof_prior_order") prior_order_asof = TSDF(prior_order_asof.df, ts_col = 'order_rcvd_ts', partition_cols = ["DATE", "TICKER"]) order_asof = prior_order_asof.asofJoin(prior_quotes_tsdf, right_prefix = "asof_order") nbbo_deltas = order_asof.df \ .withColumn("nbbo_ask_delta_direction", signum(col("asof_prior_order_ASK_PRICE") - col("asof_order_ASK_PRICE"))) \ .withColumn("nbbo_bid_delta_direction", signum(col("asof_order_BID_PRICE") - col("asof_prior_order_BID_PRICE"))) \ .withColumn("nbbo_ask_delta", abs(col("asof_prior_order_ASK_PRICE") - col("asof_order_ASK_PRICE"))) \ .withColumn("nbbo_bid_delta", abs(col("asof_order_BID_PRICE") - col("asof_prior_order_BID_PRICE"))) Below, we visualize the downward motion of the NBBO for a few sample orders, which validates the pattern we are looking for in the NBBO change. Finally, we save off the report of firms with non-bonafide executions that happen to coincide with some wash trading activity. ConclusionsIn this blueprint, we’ve focused on the ingestion of common datasets into Delta Lake as well as strategies for productionizing pipelines on Delta Lake objects. Utilizing Delta Lake enables FSIs to focus on product delivery for customers, ultimately resulting in increased AUM, decreased financial fraud, and increased subscriptions as the world of investing expands to more and more retail investors. From a technical perspective, all the use cases above are made possible by core tenets of a modern data architecture with help from the newly released tempo library: Support for open-source packages and integration with industry-accepted frameworksInfrastructure support for AI use casesFeature creation templatesTime-series analyses supportWe have documented these approaches and provided feature creation templates for a few popular use cases in the notebook links below. In addition, we’ve  introduced tempo and its applications within these templates as a foundation for investment data platforms. Try the below notebooks on Databricks to accelerate your investment platforms today and contact us to learn more about how we assist customers with similar use cases. Bloomberg API Ingestion NotebookVolatility Forecasting from Fundamental & Technical DataExecution Quality and SpoofingTry Databricks for freeGet StartedSee all Engineering Blog postsProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. 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Hao Zhu - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingHao ZhuSenior Manager at NVIDIABack to speakersHao Zhu is senior manager, accelerated Spark applications at NVIDIA. Hao and his team mainly cover customer engagement and application development for RAPIDS Accelerator for Apache Spark. Hao is experienced in Hadoop, database, massively parallel processing query engines, etc.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event.
https://www.databricks.com/p/webinar/performance-tuning-best-practices-on-the-lakehouse
Performance-Tuning Best Practices on the Lakehouse | DatabricksWebinarPerformance-Tuning Best Practices on the LakehouseInside the life of a queryHave you heard all the buzz about the lakehouse, and are you wondering what happens under the hood? How does data come in, how do you lay out and refine it for best query performance, how do you run queries, what happens during query execution and how do you optimize for world-class performance and results?Join this webinar to discover the life of a query on Databricks SQL. This webinar includes demos, live Q&As and lessons learned in the field so you can dive in and find out how to harness all the power of the Lakehouse Platform.In this webinar, you’ll learn how to:Quickly and easily ingest business-critical data into your lakehouse and continuously refine data with optimized Delta tables for best performance — no tuning requiredWrite, share and reuse queries with a native first-class SQL development experience on Databricks SQL — and unlock maximum productivityGet full transparency and visibility into query execution with an in-depth breakdown of operation-level details so you can dive inSpeakersFranco PatanoSr. Solutions ArchitectDatabricksLucas CerdanSr. Product ManagerDatabricksWatch NowProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/solutions/accelerators/digital-twins
Digital Twins | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT Goodbye, Data Warehouse. Hello, Lakehouse. Attend to understand how a data lakehouse fits within your modern data stack. Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence. See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco June 26–29   Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWSolution AcceleratorDigital TwinsPre-built code, sample data and step-by-step instructions ready to go in a Databricks notebookGet startedIncrease operational efficiency and improve decision-makingMarket dynamics and volatility are requiring manufacturers to bring products to market quicker, optimize production processes and build agile supply chains at scale at a lower price. To do so, many manufacturers have turned to building digital twins, which are virtual representations of objects, products, pieces of equipment, people, processes, or even complete manufacturing ecosystems.Digital twins are created using data derived from sensors (often IoT or IIoT) that are attached to or embedded in the original object. This data provides both structural and operational views of what happens to the object in real time, allowing engineers to monitor systems and model systems dynamics.Get started with our Solution Accelerator for Digital Twins to build performant and scalable end-to-end digital twins that:Process real-world data in real timeCompute insights at scale and deliver to multiple downstream applicationsOptimize plant operations with data-driven decisionsDownload notebooksResourcesBlogRead noweBookDownload nowWebinarLearn moreDeliver AI innovation faster with Solution Accelerators for popular industry use cases. See our full library of solutionsProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/p/webinar/deliver-real-time-retail-insights
Deliver Real-Time Retail Insights | DatabricksOn-DemandDeliver Real-Time Retail InsightsJoin us to learn how to remove common barriers to profitabilityAvailable on-demandRetailers get into trouble when they make decisions ahead of the information. But business is moving faster than ever, and traditional data platforms — such as data warehouses — weren’t designed to operate in real time.Join us to learn how the Databricks Lakehouse for Retail is helping customers like Reckitt address these architectural challenges. You’ll find out how to modernize your data and AI capabilities in ways that drive real-time decisions and enhance customer experiences.You’ll discover:Why retailers are under so much pressure to make faster decisionsHow these pressures may dictate your choice of data architectureHow Reckitt standardized on the Lakehouse for Retail to deliver insights at scaleWhat useful real-time analytics look like in actionSpeakersBryan SmithIndustry Solutions DirectorDatabricksSergiy TkachukData Science ManagerReckittSaurabh ShuklaSpecialist Solutions ArchitectDatabricksWatch NowProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights
https://www.databricks.com/br/partnerconnect
Conexão de parceiros | DatabricksSkip to main contentPlataformaDatabricks Lakehouse PlatformDelta LakeGovernança de dadosData EngineeringStreaming de dadosArmazenamento de dadosData SharingMachine LearningData SciencePreçosMarketplaceTecnologia de código abertoCentro de segurança e confiançaWEBINAR Maio 18 / 8 AM PT Adeus, Data Warehouse. Olá, Lakehouse. Participe para entender como um data lakehouse se encaixa em sua pilha de dados moderna. Inscreva-se agoraSoluçõesSoluções por setorServiços financeirosSaúde e ciências da vidaProdução industrialComunicações, mídia e entretenimentoSetor públicoVarejoVer todos os setoresSoluções por caso de usoAceleradores de soluçãoServiços profissionaisNegócios nativos digitaisMigração da plataforma de dados9 de maio | 8h PT   Descubra a Lakehouse para Manufatura Saiba como a Corning está tomando decisões críticas que minimizam as inspeções manuais, reduzem os custos de envio e aumentam a satisfação do cliente.Inscreva-se hojeAprenderDocumentaçãoTreinamento e certificaçãoDemosRecursosComunidade onlineAliança com universidadesEventosData+AI SummitBlogLaboratóriosBeaconsA maior conferência de dados, análises e IA do mundo retorna a São Francisco, de 26 a 29 de junho. ParticipeClientesParceirosParceiros de nuvemAWSAzureGoogle CloudConexão de parceirosParceiros de tecnologia e dadosPrograma de parceiros de tecnologiaPrograma de parceiros de dadosBuilt on Databricks Partner ProgramParceiros de consultoria e ISPrograma de parceiros de C&ISSoluções para parceirosConecte-se com apenas alguns cliques a soluções de parceiros validadas.Saiba maisEmpresaCarreiras em DatabricksNossa equipeConselho de AdministraçãoBlog da empresaImprensaDatabricks VenturesPrêmios e reconhecimentoEntre em contatoVeja por que o Gartner nomeou a Databricks como líder pelo segundo ano consecutivoObtenha o relatórioExperimente DatabricksAssista às DemosEntre em contatoInício de sessãoJUNE 26-29REGISTER NOWConexão de parceirosDescubra e integre facilmente dados, análises e soluções de IA com seu lakehouseAssista às demonstraçõesO Partner Connect facilita a descoberta de dados, análises e ferramentas de IA diretamente dentro da plataforma Databricks, e integra rapidamente as ferramentas que você já utiliza hoje. Com o Partner Connect, você pode simplificar a integração de ferramentas em alguns cliques e estender rapidamente as capacidades do seu lakehouse.Conecte seus dados e ferramentas de IA ao lakehouseConecte facilmente seus dados preferidos e ferramentas de IA ao lakehouse e potencialize qualquer caso de uso de análiseDescubra dados validados e soluções de IA para novos casos de usoUm portal único para soluções de parceiros validadas para você criar seu próximo aplicativo de dados mais rapidamenteConfigure em poucos cliques com integrações pré-construídasO Partner Connect simplifica suas integrações configurando automaticamente recursos, incluindo clusters, tokens e arquivos de conexão, para se conectar com soluções de parceirosComece como um parceiroOs parceiros de banco de dados estão posicionados de forma única para oferecer aos clientes insights analíticos mais rápidos. Aproveite o desenvolvimento da Databricks e recursos de parceiros para crescer ao lado de nossa plataforma aberta, baseada nas nuvens.Comece uma parceria"Com base em nossa parceria de longa data, o Partner Connect nos permite projetar uma experiência integrada entre nossas empresas e nossos clientes. Com o Partner Connect, oferecemos uma experiência simplificada que torna mais fácil do que nunca para que os milhares de clientes da Databricks, sejam usuários atuais do Fivetran ou os que nos encontrem por meio do Partner Connect, possam desbloquear insights em seus dados, descobrir mais casos de uso de análises e obter valor do seu lakehouse mais rapidamente, conectando facilmente centenas de fontes de dados ao seu lakehouse."— George Fraser, CEO da FivetranDemosFivetranConecte dados de mais de 180 aplicativos populares, incluindo aplicativos SaaS (Salesforce, Google Analytics etc.), no lakehousedbtComece com dbt Cloud e Databricks para construir transformações de dadosPower BITraga as vantagens do desempenho e da tecnologia do Databricks Lakehouse para todos os seus usuáriosTableauCapacite todos os usuários com um data lakehouse para análises modernas conectando o Tableau Desktop ao Databricks SQLRiverySimplifique a jornada dos dados de a ingestão até a transformação e entrega no Delta LakeLabelboxPrepare facilmente dados não estruturados para IA e análises no LakehouseProphecyCrie e implemente pipelines Spark e Delta usando uma interface visual de arrastar e soltarArcionConecte fontes de dados ao lakehouse com uma plataforma de replicação distribuída baseada em CDCExperimente gratuitamenteRecursosBlogFevereiro de 2023 – Anúncio de novas integrações de parceiros no Partner ConnectSetembro de 2022 – Introdução a novas integrações de parceiros no Partner ConnectJunho de 2022 – Anúncio de novas integrações de parceiros no Partner ConnectDesenvolva seus negócios na Databricks com o Partner ConnectDocumentaçãoGuia do Databricks Partner ConnectPronto para saber mais?Aproveite o desenvolvimento da Databricks e recursos de parceiros para crescer ao lado de nossa plataforma aberta, baseada nas nuvens.Comece uma parceriaEntre em contatoProdutoVisão geral da plataformaPreçosTecnologia de código abertoExperimente DatabricksDemoProdutoVisão geral da plataformaPreçosTecnologia de código abertoExperimente DatabricksDemoAprendizagem e suporteDocumentaçãoGlossárioTreinamento e certificaçãoCentral de ajudaInformações legaisComunidade onlineAprendizagem e suporteDocumentaçãoGlossárioTreinamento e certificaçãoCentral de ajudaInformações legaisComunidade onlineSoluçõesPor setorServiços profissionaisSoluçõesPor setorServiços profissionaisEmpresaQuem somosCarreiras em DatabricksDiversidade e inclusãoBlog da empresaEntre em contatoEmpresaQuem somosCarreiras em DatabricksDiversidade e inclusãoBlog da empresaEntre em contatoSee Careers at DatabricksMundialEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc. 160 Spear Street, 13th Floor San Francisco, CA 94105 1-866-330-0121© Databricks 2023. 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https://www.databricks.com/explore/de-data-warehousing/rise-of-the-data-lakehouse#page=1?itm_data=deltasharing-link-pf-riselakehousebook
Rise Of The Data Lakehouse by Bill Inmon Thumbnails Document Outline Attachments Layers Current Outline Item Previous Next Highlight All Match Case Match Diacritics Whole Words Color Size Color Thickness Opacity Presentation Mode Open Print Download Current View Go to First Page Go to Last Page Rotate Clockwise Rotate Counterclockwise Text Selection Tool Hand Tool Page Scrolling Vertical Scrolling Horizontal Scrolling Wrapped Scrolling No Spreads Odd Spreads Even Spreads Document Properties… Toggle Sidebar Find Previous Next Presentation Mode Open Print Download Current View FreeText Annotation Ink Annotation Tools Zoom Out Zoom In Automatic Zoom Actual Size Page Fit Page Width 50% 75% 100% 125% 150% 200% 300% 400% More Information Less Information Close Enter the password to open this PDF file: Cancel OK File name: - File size: - Title: - Author: - Subject: - Keywords: - Creation Date: - Modification Date: - Creator: - PDF Producer: - PDF Version: - Page Count: - Page Size: - Fast Web View: - Close Preparing document for printing… 0% Cancel