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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.
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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
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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.
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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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https://www.databricks.com/dataaisummit/ | 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. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/jp#yourprivacychoices | 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.
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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.
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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
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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.
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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
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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
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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
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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
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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
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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. |
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