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https://www.databricks.com/legal/databricks-subprocessors | Databricks Subprocessors | 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 NOWLegalTermsDatabricks Master Cloud Services AgreementAdvisory ServicesTraining ServicesUS Public Sector ServicesExternal User TermsWebsite Terms of UseCommunity Edition Terms of ServiceAcceptable Use PolicyPrivacyPrivacy NoticeCookie NoticeApplicant Privacy NoticeDatabricks SubprocessorsPrivacy FAQsDatabricks Data Processing AddendumAmendment to Data Processing AddendumSecurityDatabricks SecuritySecurity AddendumLegal Compliance and EthicsLegal Compliance & EthicsCode of ConductThird Party Code of ConductModern Slavery StatementFrance Pay Equity ReportSubscribe to UpdatesDatabricks SubprocessorsDatabricks engages certain subprocessors in connection with providing the Platform Services or Support Services. A subprocessor is a Databricks affiliate (“Databricks Affiliate'') or a third-party engaged by Databricks, Inc. or a Databricks Affiliate to process personal data on behalf of Databricks’ customers. Capitalized terms used herein have the same meaning as defined in the Master Cloud Services Agreement or other written agreement you have with Databricks (“Agreement”).Subscribe to be notified when we add new Subprocessors.Third-party SubprocessorsEntity NamePurposeEntity CountryAmazon Web Services, Inc.Cloud Service ProviderUnited StatesAmazon Data Services Ireland LtdCloud Service ProviderIrelandGoogle LLCCloud Service ProviderUnited StatesMicrosoft CorporationCloud Service ProviderUnited StatesIntercom R&D Unlimited CompanyProvide a platform for Databricks to send messages to our customers.IrelandOktaGenerate OAuth tokens (if customer does not choose to provide their own) to connect with customer-selected third parties.United StatesTwilio Inc.Provide customer communications, including in-product messaging and email.United StatesAtlassian Pty Ltd.Customer support and ticket handling.AustraliaMicrosoft CorporationCustomer support (in Microsoft Teams).United Statessalesforce.com, inc.Customer support ticketing.United StatesSlack Technologies, Inc.Customer support.United StatesSupportLogic, Inc.Monitor quality of service in responding to support tickets.United StatesDatabricks AffiliatesThe following entities are members of the Databricks Group. Accordingly, they may function as Subprocessors to provide the Databricks Services.Entity NameEntity CountryDatabricks Federal LLCUnited StatesDatabricks B.V.The NetherlandsDatabricks U.K. LimitedUnited KingdomDatabricks Australia Pty LtdAustraliaDatabricks SARLFranceDatabricks GmBHGermanyDatabricks Asiapac Unified Analytics Pte. Ltd.SingaporeDatabricks Japan K.K.JapanDatabricks India Private LimitedIndiaDatabricks Canada ULCCanadaDatabricks Italy S.r.l.ItalyDatabricks Sweden ABSwedenDatabricks Switzerland GmbHSwitzerlandDatabricks Spain S.L.SpainDatabricks Korea LLCSouth KoreaDatabricks New Zealand LimitedNew ZealandDatabricks Costa Rica SRLCosta Rica Last Modified: March 31, 2023ProductPlatform 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/larry-feinsmith | Larry Feinsmith - 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 ExperiencePricingLarry FeinsmithHead of Global Tech Strategy, Innovation and Partnerships at JP Morgan Chase & Co.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/kr/p/whitepaper/mit-cio-vision-2025?itm_data=home-promocard2-mit-cio-vision-2025 | CIO 비전 2025: BI와 AI의 간극 메우기 - Databricks보고서CIO 비전 2025BI와 AI의 간극 메우기비즈니스 가치 중심 AI 도입에 대한 글로벌 CIO 조사최고의 기업과 조직에서 AI를 마스터하는 데 가장 큰 데이터 문제를 어떻게 극복하는지 알아보세요. MIT Technology Review의 새로운 조사 보고서에서는 18개 국가와 14개 업종의 CIO 600명이 제공하는 인사이트를 확인할 수 있습니다.CIO는 AI 도입의 우선순위를 어떻게 결정하나요? 데이터 전략을 개선하기 위해 어떤 투자를 하나요?Procter & Gamble, Johnson & Johnson, Cummins, Walgreens, S&P Global, Marks & Spencer 등의 최고위 경영자와의 심층 인터뷰에서 그 답을 확인해 보세요.CIO 관점에서 주요 조사 결과 둘러보기:72%가 AI에서 가장 큰 문제가 데이터라고 답변, 68%가 분석 및 AI의 데이터 플랫폼 통합이 중요하다고 답변94%가 이미 LOB에서 AI를 사용하고 있다고 답변, 절반 이상이 2025년경에 AI가 보편화될 것으로 예상72%가 멀티클라우드가 중요하다고 생각하고 대부분이 전략적 유연성을 유지하기 위해 개방적 표준을 지지보고서 받기제품플랫폼 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.
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https://www.databricks.com/p/webinar/the-5-steps-of-hadoop-migration | Step-by-step Guide to Hadoop Migration | DatabricksOn DemandStep-by-Step Guide to Hadoop MigrationAvailable On DemandMigrating from Hadoop to a modern, cloud-based data and AI platform is a priority for more and more organizations. Join this event to learn the 5 key steps for a successful migration:How to ingest data and metadata — and how to keep that data synchronized until you are ready to EOL your on-premises solutionHow to convert your code from Hive to Apache SparkTMHow to transition your existing security (utilizing systems like Apache Ranger) to online securityHow to administer a unified data and analytics platform to remove the DevOps burdenHow to enable BI and SQL access on your cloud-based platformMigrating to a unified data and analytics platform can save you money immediately. Attendees will receive a detailed framework to help evaluate the cost and impact of migration on your organization — a great tool to help you make the case for migration.SpeakersRon GuerreroSolutions ArchitectDatabricksPardeep KumarSolutions ArchitectDatabricksMubashir KaziaSolutions 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
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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/justin-debrabant | Justin DeBrabant - 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 ExperiencePricingJustin DeBrabantSVP of Product at ActionIQBack to speakersJustin is Senior Vice President of Product at ActionIQ. He spent his formative years building large distributed systems to support data science and analytics and holds a Ph.D. in Databases from Brown University where he researched the forefront of modern data systems. For the last 10+ years, he has been passionate about building data-driven products that help realize the value of customer data by delivering truly customer-centric experiences.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/sreekanth-ratakonda | Sreekanth Ratakonda - 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 ExperiencePricingSreekanth RatakondaPrincipal Solutions Architect at LabcorpBack to speakersSreekanth Ratakonda is a principal solutions architect at Labcorp, where he is responsible for building robust Data and Analytics platform and Data products. He has over 15 years of experience in solving complex business challenges in various functional domains utilizing several technologies and frameworks.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/delta-sharing | Delta Sharing | 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 SharingAn open standard for secure sharing of data assetsGet StartedWatch DemoDatabricks Delta Sharing provides an open solution to securely share live data from your lakehouse to any computing platform.Key benefitsOpen cross-platform sharingAvoid vendor lock-in, and easily share existing data in Delta Lake and Apache Parquet formats to any data platform.Share live data with no replicationShare live data across data platforms, clouds or regions without replicating or copying it to another system.Centralized governanceCentrally manage, govern, audit and track usage of the shared data on one platform.Marketplace for data productsBuild and package data products, including data sets, ML models and notebooks once and distribute anywhere through a central marketplace.Privacy-safe data cleanroomsEasily collaborate with your customers and partners on any cloud via a secure hosted environment while safeguarding data privacy.How it worksNative integration with the Databricks platformNative integration with the Unity Catalog allows you to centrally manage and audit shared data across organizations. This lets you confidently share data assets with suppliers and partners for better coordination of your business while meeting security and compliance needs.Easily manage sharesCreate and manage providers, recipients, and shares with a simple-to-use UI, SQL commands or REST APIs with full CLI and Terraform supportDiscover and access data products through an open marketplaceEasily discover, evaluate and gain access to data products including data sets, machine learning models, dashboards and notebooks from anywhere, without the need to be on the Databricks platform.Privacy-safe data cleanroomsCollaborate with your customers and partners on any cloud in a privacy-safe environment. Securely share data from your data lakes without data replication. Meet collaborators on their preferred cloud and provide them the flexibility to run complex computations and workloads in any language — SQL, R, Scala, Java and Python. Guide collaborators through common use cases using predefined templates, notebooks and dashboards, accelerating time to insights.Use casesInternal line of business sharingBuild Data Mesh with Delta Sharing to securely share data with business units and subsidiaries across clouds or regions without copying or replicating the data.B2B SharingData monetizationCustomers“Delta Sharing helped us streamline our data delivery process for large data sets. This enables our clients to bring their own compute environment to read fresh curated data with little-to-no integration work, and enables us to continue expanding our catalog of unique, high-quality data products.”— William Dague, Head of Alternative Data“As a data company, giving our customers access to our data sets is critical. The Databricks Lakehouse Platform with Delta Sharing really streamlines that process, allowing us to securely reach a much broader user base regardless of cloud or platform.”— Felix Cheung, VP of Engineering“Leveraging the powerful capabilities of Delta Sharing from Databricks enables Pumpjack Dataworks to have a faster onboarding experience, removing the need for exporting, importing and remodeling of data, which brings immediate value to our clients. Faster results yield greater commercial opportunity for our clients and their partners.”— Corey Zwart, Head of Engineering“With Delta Sharing, our clients can access curated data sets nearly instantly and integrate them with analytics tools of their choice. The dialogue with our clients shifts from a low-value, technical back-and-forth on ingestion to a high-value analytical discussion where we drive successful client experiences. As our client relationships evolve, we can seamlessly deliver new data sets and refresh existing ones through Delta Sharing to keep clients appraised of key trends in their industries.”— Anup Segu, Data Engineering Tech LeadAn open ecosystemAccess the latest published version directly from the provider in easy-to-use SQL, Python or BI tools.ResourcesKeynote and Webinar[Keynote] Data Governance and sharing on the lakehouse at Data + AI Summit 2022[On-demand Webinar] Accelerate Business Value With Delta SharingBlogsAnnouncing General Availability of Delta SharingIntroducing Delta Sharing: An Open Protocol for Secure Data SharingTop Three Data Sharing Use Cases With Delta SharingSolution Sheet & eBook[eBook] Explore the new Delta Sharing SolutionDelta Sharing: An open standard for secure data sharingRise of the Data Lakehouse by Bill Inmon, father of the data warehouseReady to get started with Databricks?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.
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https://www.databricks.com/solutions/accelerators/survival-analysis-for-churn-and-lifetime-value | Survival Analysis for Churn and Lifetime Value | 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 AcceleratorSurvival Analysis for Churn and Lifetime ValuePre-built code, sample data and step-by-step instructions ready to go in a Databricks notebookGet startedPredict churn and calculate lifetime valueSurvival analysis is a collection of statistical methods used to examine and predict the time until an event of interest occurs. In this Solution Accelerator, learn how to use different survival analysis techniques for predicting churn and calculating lifetime value.Identify which factors contribute most to extending the customer lifecyclePredict which customers are at risk of churningOptimize marketing spend based on the ratio of lifetime value to cost of acquisitionDownload notebookResourcesWebinarWatch nowBlogRead nowDeliver 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/dataaisummit/speaker/scott-bell | Scott Bell - 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 ExperiencePricingScott BellPrincipal Consultant and Databricks SME at RapidDataBack to speakersScott is currently a Principal Consultant & Databricks SME with RapidData focusing on the Azure Data Platforms, Data Architecture, Integration Engineering and Analytics. Previously, he was a senior consultant and UK&I Databricks SME at Avanade a top global partner with 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/2022/04/05/announcing-generally-availability-of-databricks-delta-live-tables-dlt.html | Announcing General Availability of Databricks’ Delta Live Tables (DLT) - 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 SectorAnnouncing General Availability of Databricks’ Delta Live Tables (DLT)by Michael Armbrust, Awez Syed, Paul Lappas, Erika Ehrli, Sam Steiny, Richard Tomlinson, Andreas Neumann and Mukul MurthyApril 5, 2022 in Platform BlogShare this postToday, we are thrilled to announce that Delta Live Tables (DLT) is generally available (GA) on the Amazon AWS and Microsoft Azure clouds, and publicly available on Google Cloud! In this blog post, we explore how DLT is helping data engineers and analysts in leading companies easily build production-ready streaming or batch pipelines, automatically manage infrastructure at scale, and deliver a new generation of data, analytics, and AI applications.
Customers win with simple streaming and batch ETL on the LakehouseProcessing streaming and batch workloads for ETL is a fundamental initiative for analytics, data science and ML workloads – a trend that is continuing to accelerate given the vast amount of data that organizations are generating. But processing this raw, unstructured data into clean, documented, and trusted information is a critical step before it can be used to drive business insights. We’ve learned from our customers that turning SQL queries into production ETL pipelines typically involves a lot of tedious, complicated operational work. Even at a small scale, the majority of a data engineer’s time is spent on tooling and managing infrastructure rather than transformation. We also learned from our customers that observability and governance were extremely difficult to implement and, as a result, often left out of the solution entirely. This led to spending lots of time on undifferentiated tasks and led to data that was untrustworthy, not reliable, and costly.
This is why we built Delta LiveTables, the first ETL framework that uses a simple declarative approach to building reliable data pipelines and automatically managing your infrastructure at scale so data analysts and engineers can spend less time on tooling and focus on getting value from data. DLT allows data engineers and analysts to drastically reduce implementation time by accelerating development and automating complex operational tasks.
Delta Live Tables is already powering production use cases at leading companies around the globe. From startups to enterprises, over 400 companies including ADP, Shell, H&R Block, Jumbo, Bread Finance, JLL and more have used DLT to power the next generation of self-served analytics and data applications:
ADP: “At ADP, we are migrating our human resource management data to an integrated data store on the Lakehouse. Delta Live Tables has helped our team build in quality controls, and because of the declarative APIs, support for batch and real-time using only SQL, it has enabled our team to save time and effort in managing our data.” - Jack Berkowitz, Chief Data Officer at ADPAudantic: “Our goal is to continue to leverage machine learning to develop innovative products that expand our reach into new markets and geographies. Databricks is a foundational part of this strategy that will help us get there faster and more efficiently. Delta Live Tables is enabling us to do some things on the scale and performance side that we haven’t been able to do before - with an 86% reduction in time-to-market. We now run our pipelines on a daily basis compared to a weekly or even monthly basis before — that's an order of magnitude improvement.” - Joel Lowery, Chief Information Officer at AudanticShell: “At Shell, we are aggregating all our sensor data into an integrated data store. Delta Live Tables has helped our teams save time and effort in managing data at [the multi-trillion-record scale] and continuously improving our AI engineering capability. With this capability augmenting the existing lakehouse architecture, Databricks is disrupting the ETL and data warehouse markets, which is important for companies like ours. We are excited to continue to work with Databricks as an innovation partner.” - Dan Jeavons, General Manager Data Science at ShellBread Finance: "Delta Live Tables enables collaboration and removes data engineering resource blockers, allowing our analytics and BI teams to self-serve without needing to know Spark or Scala. In fact, one of our data analysts -- with no prior Databricks or Spark experience -- was able to build a DLT pipeline to turn file streams on S3 into usable exploratory datasets within a matter of hours using mostly SQL." - Christina Taylor, Senior Data Engineer at Bread FinanceModern software engineering for ETL processingDLT allows analysts and data engineers to easily build production-ready streaming or batch ETL pipelines in SQL and Python. It simplifies ETL development by uniquely capturing a declarative description of the full data pipelines to understand dependencies live and automate away virtually all of the inherent operational complexity. With DLT, engineers can concentrate on delivering data rather than operating and maintaining pipelines, and take advantage of key benefits:
Accelerate ETL development: Unlike solutions that require you to manually hand-stitch fragments of code to build end-to-end pipelines, DLT makes it possible to declaratively express entire data flows in SQL and Python. In addition, DLT natively enables modern software engineering best practices like the ability to develop in environment(s) separate from production, the ability to easily test it before deploying, deploy and manage environments using parameterization, unit testing and documentation. As a result, you can simplify the development, testing, deployment, operations and monitoring of ETL pipelines with first-class constructs for expressing transformations, CI/CD, SLAs and quality expectations, and seamlessly handling batch and streaming in a single API.Automatically manage infrastructure: DLT was built from the ground-up to automatically manage your infrastructure and automate complex and time-consuming activities. Sizing clusters for optimal performance given changing, unpredictable data volumes can be challenging and lead to overprovisioning. DLT automatically scales compute to meet performance SLAs by providing the user with the option to set the minimum and maximum number of instances and let DLT size up the cluster according to cluster utilization. In addition, tasks like orchestration, error handling and recovery, and performance optimization are all handled automatically. With DLT, you can focus on data transformation instead of operations.Data confidence: Deliver reliable data with built-in quality controls, testing, monitoring and enforcement to ensure accurate and useful BI, Data Science, and ML. DLT makes it easy to create trusted data sources by including first-class support for data quality management and monitoring tools using a feature called Expectations. Expectations help prevent bad data from flowing into tables, track data quality over time, and provide tools to troubleshoot bad data with granular pipeline observability so you get a high-fidelity lineage diagram of your pipeline, track dependencies, and aggregate data quality metrics across all of your pipelines.Simplified batch and streaming: Provide the freshest/up-to-date data for apps with data self-optimized and auto-scaling data pipelines for batch or streaming processing and choose optimal cost-performance. Unlike other products that force you to deal with streaming and batch workloads separately, DLT supports any type of data workload with a single API so data engineers and analysts alike can build cloud-scale data pipelines faster and without needing to have advanced data engineering skills.Since the preview launch of DLT, we have enabled several enterprise capabilities and UX improvements. We have extended our UI to make it easier to schedule DLT pipelines, view errors, manage ACLs, improved table lineage visuals, and added a data quality observability UI and metrics. In addition, we have released support for Change Data Capture (CDC) to efficiently and easily capture continually arriving data, as well as launched a preview of Enhanced Auto Scaling that provides superior performance for streaming workloads.
Get started with Delta Live Tables on the LakehouseWatch the demo below to discover the ease of use of DLT for data engineers and analysts alike:
To play this video, click here and accept cookiesIf you already are a Databricks customer, simply follow the guide to get started. Read the release notes to learn more about what’s included in this GA release. If you are not an existing Databricks customer, sign up for a free trial and you can view our detailed DLT Pricing here.
What's nextSign up for our Delta Live Tables Webinar with Michael Armbrust and JLL on April 14th to dive in and learn more about Delta Live Tables at Databricks.com.Try Databricks for freeGet StartedRelated posts5 Steps to Implementing Intelligent Data Pipelines With Delta Live TablesSeptember 8, 2021 by Awez Syed and Amit Kara in Platform Blog
Get an early preview of O'Reilly's new ebook for the step-by-step guidance you need to start using Delta Lake Many IT organizations are...
Announcing the Launch of Delta Live Tables on Google CloudSeptember 2, 2021 by Awez Syed and Sam Steiny in Platform Blog
Today, we are excited to announce the availability of Delta Live Tables (DLT) on Google Cloud. With this launch, enterprises can now use...
Databricks Delta Live Tables Announces Support for Simplified Change Data CaptureFebruary 10, 2022 by Michael Armbrust, Paul Lappas and Amit Kara in Platform Blog
As organizations adopt the data lakehouse architecture, data engineers are looking for efficient ways to capture continually arriving data. Even with the right t...
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https://www.databricks.com/dataaisummit/speaker/matthew-karasick | Matthew Karasick - 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 KarasickChief Product Officer at HabuBack to speakersMatt is passionate about finding ways to use data to achieve the wins that data-powered technology can create between companies and consumers.
He has spent his career helping companies do more with data. He has held product leadership positions at DoubleClick, Trilogy, Acerno, Akamai, and most recently at Indeed. After working closely together with Matt Kilmartin at Akamai, Matt (Karasick) worked with the Krux team as a consultant, where he helped create Krux for Marketers.
Matt believes that, when done correctly and with sustainable mutual value as the measuring stick, interests between consumers and companies are always aligned.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/joseph-sarsfield | Joseph Sarsfield - 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 ExperiencePricingJoseph SarsfieldMachine Learning Engineer at Marks & SpencerBack to speakersJoseph started working for M&S in June 2021 as Senior Machine Learning Engineer. He is interested in building production-ready models at scale that utilize his ML and programming background. Previously he has designed and deployed data quality models for a National Healthcare Service program and worked on human-pose estimation algorithms using depth sensors. He has a Ph.D. in machine learning and enjoys hiking in his spare time.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/nitu-nivedita/# | Nitu Nivedita - 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 ExperiencePricingNitu NiveditaManaging Director at AccentureBack to speakersNitu Nivedita is a Managing Director within Accenture’s Applied Intelligence group specializing in Artificial Intelligence, Machine Learning and Cloud Computing for scaling AI. Nitu is also the product and engineering lead for Human.AI (which is portfolio of many AI assets and engineering teams focused on building AI solutions for the Future of Work). In this role, Nitu provides technical leadership for accelerating large-scale digital transformation journeys in client organizations, building enterprise-wide AI solutions that deliver game-changing results and creating sustainable value. Nitu is passionate about technology, business and innovation. She leads a 50+ global solution team of software engineers, data scientists, ML engineers, architects, and product managers to help clients scale AI faster, innovate with cutting-edge multi-cloud technologies and thereby create 360-degree value for our communities
Nitu has a Bachelor’s of Technology (B.Tech) degree in Computer Science Engineering from National Institute of Technology, India. She has an MBA from Cornell University, New York and SDA Bocconi, Italy. She received the ‘Award of Excellence’ for achieving highest academic excellence during Computer Science Engineering, top of Dean’s list during MBA, and she was the Director of ‘Girls in Tech’ - a global organization which helps young female professionals and school-aged girls to become interested in technology, coding, and more
Nitu has been a founding member of Accenture's “Women in AI” forum. She has been awarded and/or nominated as a finalist for multiple Global AI & Technology Leadership Awards by various global organizations (VentureBeat, Women in AI etc), 2021Looking 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/de/company/partners/cloud-partners | Databricks Cloud-Partneranbieter | DatabricksSkip to main contentPlattformDie Lakehouse-Plattform von DatabricksDelta LakeData GovernanceData EngineeringDatenstreamingData-WarehousingGemeinsame DatennutzungMachine LearningData SciencePreiseMarketplaceOpen source techSecurity & Trust CenterWEBINAR 18. Mai / 8 Uhr PT
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https://www.databricks.com/company/careers/open-positions?location=berlin%2C%20germany | 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/solutions/industries/advertising-marketing | Databricks for Advertising and Marketing TechnologiesSkip 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.
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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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With Lakehouse, teams can more effectively understand audience segments for targeting, optimize inventory in real time and track the full lifecycle of an ad.Drive stronger business outcomes through collaboration
Databricks’ collaborative workspace means teams can innovate and deploy data and AI solutions faster.Unlock real-time insights
With optimized streaming ingest, pre-built offerings with top solution providers, and real-time model serving with MLflow, accelerate consumer and advertiser outcomes for your business.Rapidly innovate on predictive use cases
Databricks has a collaborative notebook environment with managed runtime for machine learning models, as well as Solution Accelerators for common use cases including audience segmentation, multitouch attribution, real-time bidding and more.Databricks for Advertising and Marketing Use CasesUse Databricks to optimize advertising performance, deliver intelligent client outcomes and improve AI across your marketing tech stack.Agency and ad tech operations
Helping companies manage margin on work and add data-focused rigor to their operations
Client profitability
Margin analysis
Sales forecasting
Talent retention
Predictive resource utilization
Media performance
Track campaign efficacy and channel performance, and leverage recommendations to remediate underperformance
Planning and media mix modeling
Multitouch attribution
Campaign performance and optimization
Taxonomy recommendation
Creative optimization
Understand what creative is resonating during campaign execution and streamline work for creative teams
A/B testing
Content performance
SEO performance
Creative workflow optimization
Transforming Advertising and Marketing With Lakehouse
“Databricks is really an important part of our AI strategy. As we continue to digitize our business, and explore opportunities and challenges through AI and machine learning, we will continue to activate Databricks throughout the rest of the business.”
— Mainak Mazumdar, Chief Research Officer at NielsenPartners and solutionsMultitouch attributionMeasure ad effectiveness and optimize marketing spend with better channel attributionGet startedSurvival analysis/LTVPredict which consumers are at risk of churning and what factors extend to consumer lifecycleGet startedPropensity to churnEffectively manage retention, understand lifecycle and reduce your churn rateGet startedBehavioral segmentationCreate advanced segments to drive better purchasing predictions based on behaviorsGet startedSales forecasting and attributionIncrease advertising outcomes by optimizing and focusing on the best-performing channelsGet startedRecommendationsIncrease conversions and engagement with personalized omnichannel recommendationsGet startedComputer vision/LabelboxAnnotate images and videos to help drive contextual ad targeting in the Lakehouse
Coming soon
Video quality of experienceAnalyze batch and streaming data to ensure a performant streaming content experienceGet startedReal-time biddingLearn how to predict ad viewability in real time to enhance your RTB strategyGet startedSee all solutionsDatabricks for advertising and marketing in actionCustomer StoryDelivering revenue-generating experiences with data and MLLearn moreCustomer TalkExtreme-scale ad tech using Spark and DatabricksLearn moreCustomer TalkHow Adobe does 2 million records per second using Apache Spark™Learn moreResourceseBooks and downloadsData-Driven Innovation in Media & EntertainmentLakehouse for Media & Entertainment Solution SheetThe Big Book of Media & Entertainment Use CasesWebinarsPushing Boundaries: Advanced M&E Case StudiesIntro to Databricks Solution Accelerators for M&ECustomer Lifetime ValuePredicting Churn to Improve Customer RetentionBlogsThe Emergence of the Composable Customer Data PlatformHow DPG Delivers High-Quality and Marketable Segments to AdvertisersReady to get started?We’d love to understand your business goals and how our services team can help you succeed.Try Databricks for freeSchedule a demoProductPlatform 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/beth-mattson/# | Beth Mattson - 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 ExperiencePricingBeth MattsonSenior Data Engineer at Our Family WizardBack to speakersPragmatic data engineer with a keen interest in data as actionable information, always striving to provide users opportunities for data discovery, creativity, and iteration. Background spans healthcare to software start-ups.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/open-source | Open Source | 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 NOWOpen sourceDatabricks engineers are the original creators of some of the world’s most popular open source data technologiesJoin a meetupOur most popular open source projectsApache Spark™Apache Spark is a unified engine for executing data engineering, data science and ML workloads.
What is Apache Spark?
Comparing Spark and Databricks
Visit spark.apache.org
Delta LakeDelta Lake lets you build a lakehouse architecture on top of storage systems such as AWS S3, ADLS, GCS and HDFS.
Learn more about Delta Lake
Visit delta.io
Tech Talks: Getting Started with Delta Lake
MLflowMLflow manages the ML lifecycle, including experimentation, reproducibility, deployment and a central model registry.
Managed MLflow on Databricks
Visit mlflow.org
Tech Talks: Managing the ML Lifecycle
RedashRedash enables anyone to leverage SQL to explore, query, visualize, and share data from both big and small data sources.
Visit Redash on GitHub
Delta SharingDelta Sharing is the industry’s first open protocol for secure data sharing, making it simple to share data with other organizations.
Visit Delta Sharing
Databricks supports these additional popular open source technologiesTensorFlowDatabricks supports TensorFlow, a library for deep learning and general computation on clusters
TensorFlow on Databricks
PyTorch™Facebook, the creator of PyTorch, and Databricks have collaborated on integrations
PyTorch on Databricks
Keras™Deep learning API written in Python, running on top of TensorFlow. Available in Databricks Runtime for ML
Keras on Databricks
RStudioAn open source suite of tools for collaborative data science using R
R programming on big data
scikit-learnWidely used Python package for machine learning built on top of NumPy, SciPy and Matplotlib
Scikit-learn on Databricks
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https://www.databricks.com/jp/solutions/data-pipelines | データエンジニアリング | 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データエンジニアリング数千万の本番ワークロードが日々 Databricks 上で実行されています無料トライアルデモを見る
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Register nowDatabricks レイクハウスプラットフォームは、バッチ/ストリーミングデータの取り込み、変換を容易にします。さらに、インフラの管理を自動化することで、信頼性の高い本番ワークフローのオーケストレーションを可能にします。また、Databricks は、データ品質の検証機能とソフトウェア開発のベストプラクティスをサポートしており、チームの生産性が向上します。バッチ/ストリーミングの両方に対応統合された API を備えた単一のプラットフォームでサイロを排除し、バッチ/ストリーミングデータの大規模な取り込み、変換、増分処理を可能にします。運用負荷の軽減Databricks は、インフラと本番ワークフローの運用コンポーネントを自動的に割り当てることで、ツールの運用管理の手間をなくします。したがって、ユーザーはデータに集中できます。任意のツールを接続レイクハウスプラットフォームのオープン性により、任意のデータエンジニアリングツールを使用したデータの取り込み、ETL/ELT、オーケストレーションが可能です。レイクハウスプラットフォームが基盤レイクハウスプラットフォームは、データ資産の構築と共有、一元管理を可能にし、高速で信頼性の高いデータソースを提供します。「私たちにとって Databricks は、あらゆる ETL 業務のワンストップショップになりつつあります。レイクハウスを活用すればするほど、ユーザー、プラットフォーム管理者の両方の負担を削減できます。」YipitData 社 エンジニアリングマネージャー Hillevi Crognale 氏仕組みデータ取り込みの簡素化ETL 処理の自動化信頼性の高いワークフローのオーケストレーションエンドツーエンドの観察・監視次世代のデータ処理エンジンガバナンス、信頼性、性能を支える基盤データ取り込みの簡素化レイクハウスプラットフォームにデータを取り込み、分析、AI、ストリーミングアプリケーションを一元管理できます。オートローダは、スケジュールされたジョブや連続したジョブにおいて、クラウドストレージにロードされたファイルに対して増分処理を自動で行います。データの状態についての詳細を手動で管理する必要はありません。数十億規模の新しいファイルでもディレクトリにリストすることなく効率的に追跡し、ソースデータからスキーマを自動的に推測し、時間の経過とともにスキーマを進化させることも可能です。アナリストは COPY INTO コマンドを使用すると、SQL を介して Delta Lake へのバッチファイルの取り込みを容易に実行できます。「データエンジニアリングの生産性が 40% 向上しました。新しい アイデアの開発にかかる時間を数日から数分に短縮し、データの可用性と精度が高まっています。」Gousto 社 最高技術責任者 Shaun Pearce 氏詳しく見るETL 処理の自動化取り込んだ未加工データは、分析や AI に利用できるように変換する必要があります。Databricks は、Delta Live Tables(DLT)により、データエンジニア、データサイエンティスト、アナリストに強力な ETL 機能を提供します。DLT は、バッチデータやストリーミングデータに ETL および ML パイプラインを構築する、シンプルな宣言型アプローチを使用した初の ETL フレームワークです。インフラ管理、タスクオーケストレーション、エラー処理やリカバリ、性能の最適化といった複雑な運用タスクを自動化します。エンジニアは DLT を使用することで、データをコードとして扱うことができ、テスト、監視、文書化などのソフトウェアエンジニアリングのベストプラクティスを適用し、信頼性の高いパイプラインを大規模に展開できます。詳しく見る信頼性の高いワークフローのオーケストレーションDatabricks Workflows は、レイクハウスプラットフォームにネイティブで、あらゆるデータ、分析、AI に対応するフルマネージド型のオーケストレーションサービスです。Delta Live Tables と、ジョブの SQL、Spark、ノートブック、dbt、ML モデルなどを含む多様なワークロードのフルライフサイクルのオーケストレーションを可能にします。基盤となるレイクハウスプラットフォームとの緊密な統合により、主要なクラウド上で信頼性の高いワークロードを作成して実行すると同時に、エンドユーザーにシンプルで詳細な一元化された監視を提供します。「私たちの使命は、地球に電力を供給する方法を変革することです。エネルギー分野のクライアントは、その変革を達成するためにデータ、コンサルティングサービス、調査 を必要としています。Databricks ワークフローは、クライアントが必要とする分析情報を提供するスピードと柔軟性を提供します。」ウッドマッケンジー社 データ部門 VP Yanyan Wu 氏詳しく見るエンドツーエンドの観察・監視レイクハウスプラットフォームによって、データと AI のライフサイクル全体が可視化されます。データエンジニアや運用チームは、本番ワークフローの健全性をリアルタイムで確認でき、データ品質の管理や、過去の傾向の把握も可能になります。Databricks Workflows では、本番ジョブと Delta Live Tables パイプラインの健全性や性能を追跡するデータフローグラフやダッシュボードにアクセスできます。また、イベントログが Delta Lake のテーブルとして公開されるため、性能、データ品質、信頼性のメトリクスをあらゆる角度から監視・可視化できます。次世代のデータ処理エンジンDatabricks のデータエンジニアリングは、Apache Spark API と互換性のある次世代エンジン Photon を実装し、数千ノードの自動スケーリングに対応すると同時に、記録的な価格性能を実現しています。Spark 構造化ストリーミングにより、バッチおよびストリーム処理の単一の統合 API が提供されるため、コードの変更や新しいスキルの習得なしに、レイクハウスでのストリーミングを容易に導入できます。詳しく見る最先端のデータガバナンス、信頼性、性能Databricks のデータエンジニアリングでは、レイクハウスプラットフォームの基本コンポーネントである Unity Catalog と Delta Lake のメリットを享受できます。Delta Lake は、ACIDトランザクションによる信頼性、スケーラブルなメタデータ処理、高速性能を提供するオープンソースのストレージフォーマットで、未加工データを最適化します。Unity Catalog と組み合わせることで、あらゆるデータと AI 資産に対するきめ細かなガバナンスを実現します。単一の一貫性のあるモデルを使用してクラウド全体でデータの発見、アクセス、共有ができるため、ガバナンスが簡素化されます。また、Unity Catalog は、他の組織と容易かつセキュアにデータを共有するための業界初のオープンプロトコル Delta Sharing をネイティブにサポートしています。データブリックスソリューションへの移行Hadoop やエンタープライズ DWH などのレガシーシステムに関連するデータサイロ、パフォーマンス低下、高いコストにうんざりしていませんか?Databricks レイクハウスに移行することで、あらゆるデータ、分析、AI のユースケースに対応する最新のプラットフォームが実現します。データブリックスソリューションへの移行統合データチームに最大限の柔軟性を提供します。Partner Connect とテクノロジーパートナーのエコシステムを活用し、主要なデータエンジニアリングツールとシームレスに統合できます。例えば、Fivetran でビジネスクリティカルなデータを取り込み、dbt を使用してインプレースで変換し、Apache Airflow でパイプラインをオーケストレーションするといったことが可能です。データインジェストと ETL+ Apache SparkTM 互換クライアント導入事例さらに詳しくDelta LakeワークフローDelta Live TablesDelta Sharing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eBookデータレイクハウスの構築(データウェアハウス提唱者 Bill Inmon 著)データ、分析、AI ガバナンスData Management 101(データ管理の基礎)データエンジニアリングのビッグブックeBook:新しい Delta Sharing ソリューションの詳細データウェアハウスからデータレイクハウスへの移行 ― "For Dummies" シリーズイベントDelta Live Tables を活用したデータ変換データ取り込みを簡単にする方法DATA + AI SUMMIT 2022データウェアハウスをモダナイズブログDelta Live Tables の一般提供開始を発表Databricks Workflows のご紹介Databricks と Apache Spark 向け 2021 年に開発された構造化ストリーミング新機能の概要Databricks レイクハウスにおける半構造化データの管理をシンプルにする 10 の強力な機能無料お試し・その他ご相談を承ります無料トライアルコミュニティに参加するAWSAzureGCP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/it/partnerconnect | Partner Connect | 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 NOWPartner ConnectScopri e integra facilmente soluzioni per dati, analisi e AI con il lakehouseGuarda le demoPartner Connect aiuta a scoprire strumenti di gestione e analisi dei dati e di AI sulla piattaforma Databricks, integrandoli velocemente con gli strumenti già in uso. Con Partner Connect, per l'integrazione di nuovi strumenti bastano pochi clic e le funzionalità del lakehouse possono essere ampliate velocemente.Collegare al lakehouse gli strumenti per la gestione di dati e AIConnetti facilmente i tuoi strumenti preferiti per dati e AI al lakehouse e alimenta qualsiasi caso d'uso di analisiSoluzioni validate per dati e AI per nuovi casi d'usoUn portale unico con le soluzioni validate di aziende partner per costruire più velocemente le prossime applicazioni di gestione dei datiImpostazione in pochi clic con integrazioni predefinitePartner Connect semplifica le integrazioni configurando automaticamente le risorse (inclusi cluster, token e file di connessione) per la connessione con le soluzioni dei nostri partnerPrimi passi come partnerI partner di Databricks sono in una posizione unica per offrire ai clienti analisi approfondite più velocemente. Sfrutta le risorse di sviluppo e le partnership di Databricks per crescere insieme alla nostra piattaforma aperta in cloud.Diventa partner“Sfruttando la nostra partnership di lunga data, Partner Connect ci consente di sviluppare un'esperienza integrata fra le nostre aziende e i clienti. Con Partner Connect, offriamo un'esperienza ottimizzata che semplifica più che mai la vita alle migliaia di clienti di Databricks, sia coloro che già utilizzano Fivetran, sia coloro che ci scoprono tramite Partner Connect, per estrapolare informazioni dai loro dati, scoprire nuovi casi d'uso dell'analisi ed estrarre valore dal loro lakehouse più velocemente, collegando con facilità centinaia di sorgenti di dati al lakehouse”.— George Fraser, CEO di FivetranDemofivetranConnettere dati da oltre 180 app popolari, incluse app SaaS (Salesforce, Google Analytics ecc.) all'interno del lakehousedbtPrimi passi con dbt Cloud e Databricks per la trasformazione dei datiPower BIPorta i vantaggi delle prestazioni e della tecnologia Databricks Lakehouse a tutti gli utentitableauMetti a disposizione di tutti gli utenti un data lakehouse per analisi moderne collegando Tableau Desktop a Databricks SQLRiverySemplifica il percorso dei dati dall'acquisizione alla trasformazione, fino al trasferimento in Delta LakeLabelboxPrepara facilmente dati non strutturati per AI e analisi nel lakehouseProphecyCostruire e implementare pipeline Spark e Delta utilizzando un'interfaccia visuale drag-and-dropArcioneConnetti le sorgenti di dati al lakehouse con una piattaforma di replica distribuita basata su CDCProva gratuitaRisorseBlogFeb 2023 - Annuncio di nuove integrazioni per i partner in Partner Connect2022 settembre - Introduzione di nuove integrazioni per i partner in Partner ConnectGiugno 2022 - Annuncio di nuove integrazioni per i partner in Partner ConnectSviluppa la tua attività su Databricks con Partner ConnectDocumentazioneGuida a Partner Connect di DatabricksPronto per saperne di più?Sfrutta le risorse di sviluppo e le partnership di Databricks per crescere insieme alla nostra piattaforma aperta in cloud.Diventa partnerContattiProdottoPanoramica 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.
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https://www.databricks.com/dataaisummit/speaker/jules-damji | Jules Damji - 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 ExperiencePricingJules DamjiLead Developer Advocate at AnyscaleBack to speakersJules S. Damji is a lead developer advocate at Anyscale Inc, an MLflow contributor, and co-author of Learning Spark, 2nd Edition. He is a hands-on developer with over 25 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, Opsware/LoudCloud, VeriSign, ProQuest, Hortonworks, and Databricks, building large-scale distributed systems.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/reynold-xin | Reynold Xin - 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 ExperiencePricingReynold XinCo-founder and Chief Architect at DatabricksBack to speakersReynold is an Apache Spark™ PMC member and the top contributor to the project. He initiated and led efforts such as DataFrames and Project Tungsten. He is also a co-founder and Chief Architect at 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/dataaisummit/speaker/jonathan-neo | Jonathan Neo - 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 NeoData Engineer at CanvaBack to speakersJonathan is a Data Engineer at Canva where he is building data platforms that scale for petabytes of data and hundreds of internal users. Prior to Canva, he has spent the last 5 years building a dozen data platforms for ASX-listed enterprises, and startups. Jonathan is also the Founder of Data Engineer Camp, a 16 week intensive bootcamp to cultivate the next generation of data engineers, with students working at companies like Microsoft, Tencent, and Fidelity International.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/vika-smilansky/# | Vika Smilansky - 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 ExperiencePricingVika SmilanskyDirector of Product Marketing at ThoughtSpotBack to speakersVika is a Director of Product Marketing at ThoughtSpot, leading customer marketing as well as messaging and positioning for ThoughtSpot’s embedded analytics platform, ThoughtSpot Everywhere. Before joining Thoughtspot in 2019, she worked in Product Marketing on data integration solutions at Oracle. Vika holds a Master in Communication Management from the University of Southern California.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/de/solutions/audience/digital-native | Digital-Native-Unternehmen und -Anwendungen | 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 NOWLakehouse für Unternehmen, die in der Cloud geboren wurdenSchnellere Entwicklung und Skalierung von Daten-, Analytics- und KI-Funktionen auf einer leistungsstarken Datenplattform
Erste SchritteDemos ansehenJe schneller Sie wachsen, desto komplexer werden Ihre Daten. Schnellere Innovationen auf der Databricks Lakehouse-Plattform – ein einfacher, kosteneffizienter Ansatz für Daten, Analysen und KI, der Tausende von digital nativen Unternehmen und Start-ups produktiver macht.Innovation mit Open-Source-Flexibilität
Nutzen Sie Ihre Daten doch, wie und wo Sie wollen – ganz ohne Anbieterbindung. Dafür haben die Entwickler von Apache Spark™ ein Lakehouse geschaffen, das offene Formate und APIs unterstützt.
Open-Source-Projekte anzeigenSkalierbare Daten-Workloads aufbauen
Sorgen Sie für rasante und zuverlässige Leistung bei ETL-Workloads – für Streaming und Batch-Daten. Währenddessen verwaltet Databricks Ihre Infrastruktur automatisch.
Data Engineering anzeigenSchneller auf Daten zugreifen
Alle Daten erfassen, transformieren und abfragen – komplett von zentraler Stelle aus. Schluss mit dem Verwalten von Servern – bei Bedarf skalieren Sie eben per Serverless. Bis zu 12 Mal besseres Preis-Leistungs-Verhältnis.
Databricks SQL anzeigenNext-Gen-Apps mit ML entwickeln
Beschleunigen Sie den ML-Lebenszyklus vom Experiment bis zur Produktion. Steigern Sie die Produktivität mit Tools wie kollaborativen Notebooks, MLflow und MLOps.
ML-Funktionen anzeigenErste Schritte mit DatabricksDatabricks testen
Starten Sie Ihre 14-tägige kostenlose Testversion mit Databricks on AWS in wenigen einfachen Schritten. Suchen und finden Sie Ressourcen für sich und Ihr gesamtes Datenteam.
Jetzt kostenfrei testenKompetenzen entwickeln
Absolvieren Sie auf Ihre Bedürfnisse zugeschnittene Schulungen und Zertifizierungen, einschließlich Data Analytics, Data Engineering, Data Science und Machine Learning.
Schulungen abrufenKosten optimieren
Mit einem verwalteten Dienst können Sie die Infrastrukturkosten um bis zu 40 % senken und gleichzeitig Ihre Produktivität steigern. Informieren Sie sich über unsere nutzungsbasierte Abrechnung und die ermäßigten Tarife.
Preise anzeigenTechnischer Support
Mit unseren Databricks-Experten erhalten Sie notwendige Antworten schneller. Außerdem können Sie Ihre Datenlösung mit unseren Selfservice-Notebooks im Handumdrehen entwickeln.
Notebooks anzeigenAufbauprogrammeErstellen Sie Ihre datengesteuerten Anwendungen auf dem Lakehouse und beschleunigen Sie Ihr Geschäftswachstum mit DatabricksDatabricks für StartupsStarten Sie schnell mit dem Startprogramm. Erfahren Sie, wie Sie kostenlose Guthaben, Expertenratschläge und Unterstützung bei der Markteinführung erhalten.Mehr InformationenAufbaupartner Werden Sie ein integrierter Partner, um Zugang zu technischen, Markteinführungs- und Co-Marketing-Vorteilen zu erhalten, mit denen Sie Ihre Reichweite vergrößern und Ihr Geschäft ausbauen können.Mehr InformationenUnternehmen der nächsten Generation, die auf Databricks setzenLösungsarchitekturen für Digital-Native-UnternehmenLeistungsstarke, skalierbare ETL-PipelinesErstellen Sie eine konsistente Data-Engineering- und ETL-Plattform, um sich in jeder Cloud ganz der Gewinnung wertvoller Erkenntnisse zu widmen. Nie wieder Pipelines entwickeln und warten, nie wieder ETL-Workloads ausführen.Nutzen Sie produktionsfertige Tools wie Delta Live Tables, Unity Catalog und Workflows.Genießen Sie robuste Git-Integration, Orchestrierung und Datenqualitätskontrollen.Vereinheitlichen Sie Batch- und Streaming-Operationen in einer vereinfachten Architektur und optimieren Sie Entwicklung und Tests von Daten-Pipelines.Gewährleisten Sie höchste Datenqualität und verbessertes Data Skipping mit Delta Lake – einem Open-Source-Dateiprotokoll, das von Apache Spark, Trino, Presto, Flink u. a. verwendet werden kann.Erste SchritteSQL-Analytics und Data WarehousingDaten erfassen, transformieren und abfragen von zentraler Stelle aus – so gewinnen Sie im Nu geschäftliche Echtzeiterkenntnisse.Führen Sie auch umfangreichste SQL- und BI-Anwendungen aus – mit einem bis zu 12 Mal besseren Preis-Leistungs-Verhältnis. Außerdem garantieren Sie so Data Governance und Sicherheit.Bewältigen Sie High Concurrency mit vollständig verwaltetem Load Balancing und Skalierung der Compute-Ressourcen.Nutzen Sie offene Formate und APIs sowie Erfassung, Transformation und BI-Tools nach Wahl mit maßgeschneiderten Konnektoren.Reduzieren Sie den Aufwand für das Ressourcenmanagement mit Serverless Compute.Erste SchritteInnovatives Machine LearningBeschleunigen Sie ML und Data Science durch Ausbau von Produktivität und Kollaboration im Lakehouse.Nutzen Sie Tools und Optionen für die Zusammenarbeit mit Glass-Box-AutoML.Mit einem gehosteten Feature Store können Sie Daten und Funktionen im Self-Service vorbereiten, verarbeiten und verwalten und auch Ihre Modelle managen.Standardisieren Sie den ML-Lebenszyklus vom Experiment bis zur Produktion mithilfe von MLflow, um Modellparameter, Metriken und Iterationen im zeitlichen Verlauf zu beobachten.Stellen Sie Modelle im Batch oder mit Serverless-Echtzeit-REST-Endpoints bereit.Erste SchritteTechnologiepartner
Entdecken und integrieren Sie Ihre Lieblingsdaten und KI-Tools und -Dienste direkt auf der Databricks-Plattform.
Partner Connect erkundenBranchenlösungenNext-Generation-Branchenführer entwickeln Datenanalyse- und KI-Lösungen mit DatabricksLösungen nach Branche erkundenEinzelhandel und Konsumgüter
Fördern Sie Echtzeitentscheidungen und verbessern Sie das Kundenerlebnis.
Mehr InformationenFinanzdienstleistungen
Machen Sie Ihre daten- und KI-gesteuerten Finanzdienstleistungen fit für die Zukunft.
Mehr InformationenGaming
Nutzen Sie die Leistungsstärke von Daten und KI, um Spielerlebnisse auf einem noch nie dagewesenen Niveau zu erschließen.
Mehr InformationenKommunikation, Medien und Unterhaltung
Erstellen Sie daten- und KI-gestützte Medien für ein Benutzererlebnis in beispielloser Qualität.
Mehr InformationenTechnologie und Software
Entwickeln Sie Lösungen der nächsten Generation mit erweiterten Daten- und ML-Funktionen.
Mehr InformationenTechnologie im Gesundheitswesen
Mit der Leistungsfähigkeit von Daten und KI erzielen Sie bessere Ergebnisse für Ihre Patienten.
Mehr InformationenTechnischer LeitfadenLösung gemeinsamer DatenherausforderungenFür Startups und Digital NativesErfahren Sie, wie Sie Datenanwendungsfälle bei der Skalierung unterstützen und gleichzeitig die Kosteneffizienz und Produktivität steigern können. Sie profitieren von Architekturdiagrammen, Schritt-für-Schritt-Lösungen und Schnellstartanleitungen. Sie finden auch Anwendungsfälle aus der Praxis von führenden Unternehmen wie Grammarly, Rivian, ButcherBox, Abnormal Security, Iterable und Zipline.Holen Sie sich Ihr ExemplarRessourcenE-BooksWas ist ein Lakehouse?Das große Buch des Data EngineeringDas Big Book der Machine-Learning-AnwendungsfälleDer ultimative Leitfaden zu Delta LakeDelta Lake – die Grundlage für unser LakehouseDas große Buch der MLOpsDatenteam-Leitfaden zur Databricks Lakehouse-PlattformSchnellstart-LeitfädenETL-Workloads ausführenDatenpipelines mit Delta Live TablesSQL-Leitfaden: Querys und DashboardsData Science-SchnellstartMachine Learning-SchnellstartBlogsAnkündigungenOpen-SourceBranchenerkenntnisseLösungenMöchten Sie loslegen?Kostenlose Testversion abrufenProduktPlatform 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.
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https://www.databricks.com/dataaisummit/speaker/pravin-darbare | Pravin Darbare - 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 ExperiencePricingPravin DarbareVice President of Data Analytics at WorkdayBack to speakersComing soonLooking 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/shell | Customer Story: Shell | 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 STORYDelivering innovative energy solutions for a cleaner worldMillionsof dollars saved in potential engine repair costs250Data team members supporting 160+ high-value use cases9xFaster – 5 minutes to validate a label reduced from 45 minutesRead storyWatch video INDUSTRY: Energy and utilities SOLUTION: Data-driven ESG,demand forecasting,predictive maintenance,safety stock analysis,threat detection PLATFORM USE CASE: Lakehouse,Delta Lake,data science,machine learning,ETL,Databricks SQL CLOUD: Azure“The usage of Databricks over the years has broadened significantly. We started out using Databricks as a big data and AI platform but the scope has broadened. We have an entirely different class of citizen engineers and data scientists who are using it as a modern business intelligence tool to make smarter business decisions.”
— Daniel Jeavons, General Manager – Advanced Analytics CoE, ShellShell has been at the forefront of creating a cleaner tomorrow by investing in digital technologies to tackle climate change and become a net-zero emissions energy business. Across the business, they are turning to data and AI to improve operational efficiencies, drive customer engagement, and tap into new innovations like renewable energy. Hampered by large volumes of data, Shell chose Databricks to be one of the foundational components of its Shell.ai platform. Today, Databricks empowers hundreds of Shell’s engineers, scientists and analysts to innovate together as part of their ambition to deliver cleaner energy solutions more rapidly and efficiently.The challenges of extracting insights at scaleThroughout its 100-plus-year history, Shell has generated pioneering ideas that have influenced the way we consume energy.“We, as an industry, are going through a massive transition,” explained Dan Jeavons, GM of data science at Shell. “Digital technology is absolutely core to making our existing business more effective and efficient. As the industry continues to expand into new areas of energy that are more sustainable and reduce environmental impact, data and digital technology are now table stakes.”While digital transformation is a primary initiative for every energy company, challenges remain with legacy technology infrastructure, the complexities of an exponential growth in data, and the lack of data engineering and science skills needed to build data-powered solutions.Shell has met these challenges head-on by creating a Data Science Centre of Excellence (CoE), where teams continually work to identify the highest value use cases across the entire value chain. However, although they were identifying opportunities to innovate with data, Shell had the challenge to scale its data infrastructure for analytics, big data processing and machine learning.Unifying data and AI across the enterprise to fuel innovationShell chose the Databricks Lakehouse Platform as one of the key tools within the Shell.ai Platform. Databricks provides Shell’s data team with a scalable, fully managed platform that unifies all their data, analytics and AI workloads. The interactive workspace has not only democratized access to data but has fostered cross-team collaboration across data engineering, data science and the analyst team.“Shell has been undergoing a digital transformation as part of our ambition to deliver more and cleaner energy solutions. As part of this, we have been investing heavily in our data lake architecture. Our ambition has been to enable our data teams to rapidly query our massive datasets in the simplest possible way. The ability to execute rapid queries on petabyte scale datasets using standard BI tools is a game changer for us. Our co-innovation approach with Databricks has allowed us to influence the product roadmap and we are excited to see this come to market,” said Dan.This low barrier of entry has opened up analytics beyond machine learning, including business intelligence and reporting. In fact, Shell’s focus on data and analytics has enabled over 250 data analysts (or citizen data scientists), and 800 citizen data scientists to be more productive with all the data available to them.Transforming Shell into the energy company of the futureShell’s CoE is now able to explore and deploy new data-driven solutions focused on improving supply chain operations as well as unlocking high-valued use cases that bring to life differentiated capabilities for their customers and their own businesses.From an operations perspective, one of the biggest challenges any major industrial company faces is efficiently managing its inventory and supply chain. Shell stocks thousands of spare parts across its global facilities, and its inventory analysts were struggling to understand what level of spare parts they should hold in their warehouses. With Databricks, Shell was able to leverage its full historic data set to run 10,000+ inventory simulations across all its parts and facilities. Shell’s inventory prediction models now run in 45 minutes — down from 48 hours — significantly improving stocking practices and saving a lot of money annually.Shell has also developed a recommendation engine for its new loyalty program called Go+ used by 1.5 million customers. Running on Azure and Databricks, the AI software can look at the full transaction history of a customer and use the information to tailor the offers and rewards to the preferences of the individual, combining their data with other aggregated data.Data and AI have also unlocked new opportunities for Shell to engage with customers. Shell Remote Sense is a new initiative focused on optimizing the durability and performance of large-scale engines on ships and cruise liners. Shell processes over 750,000 lubricant samples per annum and delivers customer insights about lube oil quality and how it’s performing. This not only saves customers potentially millions of dollars in the cost of repair or engine downtime, but Shell also saves significantly on time and operational costs.A data-driven culture that delivers resultsToday Shell is redefining its boundaries of the oil and gas industry through data and AI. With Databricks as a key component of the Shell.ai platform, Shell is able to run data analytics and deploy machine learning models that improve operational efficiencies.Using a common platform has empowered engineers, data scientists and analysts to be more agile, collaborative and data driven. Shell currently has over 160 AI projects running, and it’s only just getting started. In the coming years, Shell aims to make leaps in technological advancements powered by data and AI — from trillions of IoT sensors all generating data to 3-D printed equipment and parts that will disrupt the global supply chain and greatly reduce costs— and Databricks is a key part of the Shell.ai platform that will make this a reality.Related ContentCase StudyETL and Data Lake with Delta Live TablesVideoAI and Sustainability 2021 Web Summit PanelArticleCIO Magazine: Shell sees AI as fuel for its sustainability goalsReady 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.
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https://www.databricks.com/dataaisummit/speaker/justin-lai | Justin Lai - 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 ExperiencePricingJustin LaiDistinguished Data Architect at BlackBerryBack to speakersJustin is Software Architect at BlackBerry for over 12 years and have experience working in many areas ranging from low level network stack on BlackBerry Devices, Android application development, to Cloud Data engineering.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/stephen-shelton | Stephen Shelton - 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 ExperiencePricingStephen SheltonVice President, Business Intelligence at Pluto TVBack 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/rashmi-kansakar/# | Rashmi Kansakar - 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 ExperiencePricingRashmi KansakarDirector, Data & Cloud Architect at 84.51Back to speakersAs Director of Data & Cloud Architect at 84.51˚, Rashmi is responsible for designing next-generation data solutions focusing on identifying needs and opportunities to elevate & harmonize data into valuable data assets. He helps to build scalable technology to drive insights and science to understand Kroger’s customers better and improve the shopping experience. Rashmi is as an Adjunct Professor at the University of Cincinnati, where he shares his knowledge and expertise with the next generation.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/kr/solutions/data-engineering | 데이터 엔지니어링 | 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 NOW데이터 엔지니어링매일 수천 만 개의 프로덕션 워크로드가 Databricks에서 실행시작하기데모 보기
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Register nowDatabricks 레이크하우스 플랫폼에서 배치 및 스트리밍 데이터를 간편하게 수집하고 변환할 수 있습니다. Databricks에게 인프라의 대규모 자동 관리를 맡기고 안정적인 프로덕션 워크플로를 오케스트레이션하세요. 기본 데이터 품질 테스트 기능과 소프트웨어 개발 모범 사례 지원으로 팀 생산성을 향상할 수 있습니다.배치와 스트리밍 통합하나의 통합 API를 사용하는 단일 플랫폼을 통해 사일로를 제거하고 대규모로 배치 및 스트리밍 데이터를 수집, 변환 및 증분 처리할 수 있습니다.데이터 가치 창출에 집중Databricks가 인프라와 프로덕션 워크플로의 운영 부문을 자동으로 관리하므로 고객은 툴링이 아니라 가치 창출에 집중할 수 있습니다.원하는 도구와 연결개방적 레이크하우스 플랫폼에서 데이터 수집, ETL/ELT, 오케스트레이션에 사용하고 싶은 데이터 엔지니어링 도구를 연결하고 사용할 수 있습니다.레이크하우스 플랫폼 기반레이크하우스 플랫폼은 신뢰할 수 있는 데이터 자산을 구축 및 공유하는 데 가장 적절한 기반을 제공하며, 이러한 데이터 자산은 중앙에서 관리하고 안정적이면서도 매우 빠른 속도를 자랑합니다.“우리에게 Databricks는 모든 ETL 작업을 처리하는 원스톱 샵이 되었습니다. 레이크하우스 플랫폼을 많이 사용할수록 사용자와 플랫폼 관리자 모두에게 훨씬 편리해집니다.”— Hillevi Crognale, 엔지니어링 관리자, YipitData어떻게 작동하나요?데이터 수집 간소화자동 ETL 처리안정적인 워크플로 오케스트레이션전체적 관찰 기능 및 모니터링차세대 데이터 처리 엔진거버넌스, 안정성 및 성능의 기반데이터 수집 간소화레이크하우스 플랫폼에 데이터를 입력하고 한 곳에서 분석, AI 및 스트리밍 애플리케이션을 지원해 보세요.Auto Loader는 클라우드 스토리지에 저장되는 파일을 증분 방식으로 자동 처리하므로 상태 정보를 예약 작업이나 연속적 작업으로 관리할 필요가 없습니다. 디렉터리에서 모니터링하지 않아도 새로운 파일을 효율적으로 추적하며(수십억 개까지 확장 가능), 소스 데이터에서 스키마를 자동 추론하여 나중에 변경이 발생하면 그에 맞춰 조정합니다. COPY INTO 명령을 사용하면 애널리스트가 SQL을 통해 손쉽게 Delta Lake로 배치 파일을 수집할 수 있습니다.“데이터 엔지니어링의 생산성이 40% 향상되어서 새로운 아이디어를 개발하기까지 걸리는 시간이 며칠에서 몇 분으로 단축된 데다, 데이터 가용성과 정확성이 높아졌습니다.”— Shaun Pearce, 최고 기술 책임자, Gousto자세히자동 ETL 처리파일이 수집되고 나면 분석과 AI에 사용할 수 있도록 가공되지 않은 데이터를 변환해야 합니다. Databricks는 Delta Live Tables(DLT)로 데이터 엔지니어, 데이터 사이언티스트, 애널리스트에게 강력한 ETL 기능을 제공합니다. DLT는 간단한 선언적 방식으로 배치 또는 스트리밍 데이터를 위한 ETL 및 ML 파이프라인을 구축하는 최초의 프레임워크이며, 인프라 관리나 작업 오케스트레이션, 오류 처리, 복구와 같은 운영 복잡성과 성능 최적화를 자동화합니다. DLT를 사용하는 엔지니어는 데이터를 코드로 처리할 수 있고, 테스트, 모니터링 및 문서화 등의 소프트웨어 엔지니어링 모범 사례를 적용하여 대규모로 안정적인 파이프라인을 배포할 수 있습니다.자세히안정적인 워크플로 오케스트레이션Databricks Workflows는 모든 데이터, 분석, 레이크하우스 플랫폼에 네이티브인 AI에 대한 완전 관리형 오케스트레이션 서비스입니다. Delta Live Tables, Jobs for SQL, Spark, 노트북, dbt, ML 모델 등을 포함한 전체 수명 주기에 대해 다양한 워크로드를 오케스트레이션합니다. 기존 레이크하우스 플랫폼과 긴밀히 통합되므로 모든 클라우드에서 안정적인 프로덕션 워크로드를 생성 및 실행하면서도 최종 사용자에게 간단하게 심층적 중앙 집중형 모니터링을 제공합니다."우리의 임무는 지구에 전력을 공급하는 방식을 변화시키는 것입니다. 에너지 부문의 고객은 이러한 변화를 달성하기 위해 데이터, 컨설팅 서비스 및 연구가 필요합니다. Databricks 워크플로는 고객이 필요로 하는 인사이트를 제공할 수 있는 속도와 유연성을 제공합니다."— Yanyan Wu, 데이터 부문 부사장, Wood Mackenzie자세히전체적 관찰 기능 및 모니터링레이크하우스 플랫폼은 모든 데이터와 AI 수명 주기에 대한 가시성을 제공하므로, 데이터 엔지니어와 운영 팀에서 실시간으로 프로덕션 워크플로 상태를 확인하고, 데이터 품질을 관리하며, 과거의 트렌드를 파악할 수 있습니다. Databricks Workflows에서는 프로덕션 작업과 Delta Live Tables 파이프라인의 상태와 성능을 추적하는 데이터 플로 그래프 및 대시보드에 액세스할 수 있습니다. 이벤트 로그는 Delta Lake 테이블로 노출되어, 모든 각도에서 성능과 데이터 품질, 안정성 지표를 모니터링하고 시각화할 수 있습니다.차세대 데이터 처리 엔진Databricks 데이터 엔지니어링은 Apache Spark API와 호환되는 차세대 엔진인 Photon을 기반으로 하여 수천 개의 노드로 자동 확장하면서도 독보적인 가격 대비 성능을 제공합니다.Spark Structured Streaming은 배치 및 스트리밍 처리에 하나의 통합된 API를 제공합니다. 코드를 변경하거나 새로운 기술을 배우지 않고도 레이크하우스에서 손쉽게 스트리밍을 도입할 수 있습니다.자세히최첨단 데이터 거버넌스, 안정성 및 성능Databricks에서 데이터 엔지니어링을 사용하면 레이크하우스 플랫폼의 기본 구성 요소(Unity Catalog 및 Delta Lake)를 활용할 수 있게 됩니다. ACID 트랜잭션을 통해 안정성을 제공하고 확장 가능한 메타데이터를 매우 빠른 속도로 처리하는 오픈 소스 스토리지 형식인 Delta Lake로 가공되지 않은 데이터를 최적화합니다. 여기에 Unity Catalog를 결합하면 모든 데이터와 AI 자산에 세분화된 거버넌스를 제공할 수 있을 뿐만 아니라, 모든 클라우드에서 일관적인 데이터 탐색, 액세스, 공유 모델을 적용하여 거너번스 방식을 단순화합니다. 또한, Unity Catalog는 다른 조직과 간단하고 안전하게 데이터를 공유할 수 있는 업계 최초의 오픈 프로토콜인 Delta Sharing을 지원합니다.Databricks로 마이그레이션데이터 사일로, 느린 성능, Hadoop이나 엔터프라이즈 데이터 웨어하우스에서 발생하는 높은 비용에 지치셨나요? 모든 데이터, 분석 및 AI 사용 사례를 위한 현대적 플랫폼, Databricks 레이크하우스로 마이그레이션하세요.Databricks로 마이그레이션통합데이터 팀에 최대의 유연성을 제공할 수 있습니다. Partner Connect 및 기술 파트너 에코시스템을 활용하여 일반적으로 사용하는 데이터 엔지니어링 도구와 매끄럽게 통합해 보세요. 예를 들어 Fivetran으로 비즈니스에 중요한 데이터를 수집하고, dbt로 바로 변환하여, Apache Airflow로 파이프라인을 오케스트레이션할 수 있습니다.데이터 수집 및 ETL+ 여타 모든 Apache Spark™️ 호환 클라이언트고객 사례더 자세히 알아보기Delta Lake워크플로우Delta Live 테이블Delta Sharing관련 콘텐츠
여러분에게 필요한 모든 리소스가 한 곳에 있습니다.
리소스 라이브러리의 ebook과 동영상을 통해 Databricks에서 데이터 엔지니어링을 활용하는 장점에 대해 알아보세요.
리소스 둘러보기eBook데이터 웨어하우스의 아버지 Bill Inmon이 구축한 데이터 레이크하우스데이터, 분석 및 AI 거버넌스데이터 관리 기초데이터 엔지니어링 Big Book새로운 Delta Sharing 솔루션 알아보기왕초보를 위한 데이터 웨어하우스에서 데이터 레이크하우스로 마이그레이션이벤트Delta Live Tables로 데이터 변환 처리간편한 데이터 수집 웨비나 시리즈DATA + AI SUMMIT 2022데이터 웨어하우스의 현대화블로그Databricks Delta Live Tables(DLT) 정식 출시Databricks Workflows 소개2021년에 Databricks 및 Apache Spark용으로 개발된 새로운 구조화 스트리밍 기능 전체 개관Databricks 레이크하우스에서 반구조화된 데이터 과니를 단순화하기 위한 10가지 강력한 기능시작할 준비가 되셨나요?무료 시험판사용해 보기커뮤니티 가입AWSAzureGCP제품플랫폼 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
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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 NOWLegalTermsDatabricks Master Cloud Services AgreementAdvisory ServicesTraining ServicesUS Public Sector ServicesExternal User TermsWebsite Terms of UseCommunity Edition Terms of ServiceAcceptable Use PolicyPrivacyPrivacy NoticeCookie NoticeApplicant Privacy NoticeDatabricks SubprocessorsPrivacy FAQsDatabricks Data Processing AddendumAmendment to Data Processing AddendumSecurityDatabricks SecuritySecurity AddendumLegal Compliance and EthicsLegal Compliance & EthicsCode of ConductThird Party Code of ConductModern Slavery StatementFrance Pay Equity ReportSubscribe to UpdatesMaster Cloud Services AgreementThis Master Cloud Services Agreement (the “MCSA”) is entered into as of the Effective Date between Databricks, Inc. (“Databricks” or “we”) and Customer (as defined below) and governs Customer’s use of the Databricks Services, including the right to access and use the Databricks data processing platform services (the “Platform Services”), on each cloud service where Databricks directly provides customers with access to such Platform Services. For the avoidance of doubt, this Agreement does not govern the use of Databricks Powered Services. Unless otherwise indicated, capitalized terms have the meaning assigned to them in this MCSA or in an incorporated Schedule.If you are entering into this MCSA on behalf of a company (such as your employer) or other legal entity, you represent and warrant that You are authorized to bind that entity to this MCSA, in which case “Customer,” “you,” or “your” will refer to that entity (otherwise, such terms refer to you as an individual). If you do not have authority to bind Your entity or do not agree with any provision of this MCSA, you must not accept this MCSA and may not use the Databricks Services.By accepting this MCSA, either by executing this MCSA, an Order, or another agreement that explicitly incorporates this MCSA by reference, Customer enters into the MCSA and the following Schedules, each of which are incorporated into the MCSA and apply to the provision of the applicable Databricks Services upon your ordering such service:Advisory ServicesTraining ServicesU.S. Public Sector ServicesYour Order may include one or more of the following: (a) the Platform Services, (b) support services (“Support Services“), (c) training services (“Training Services“), or (d) advisory services (“Advisory Services,” and together with any other services provided by Databricks, (a), (b), (c), and (d) shall be defined as the “Databricks Services”). You acknowledge that no term in any Order entered into via a reseller will be deemed to modify the Agreement unless pre-authorized in writing by Databricks. Definitions. Defined terms are set out below. Capitalized terms used but not defined in a Schedule or an Order will have the meaning assigned to them, if any, within this MCSA.
“Acceptable Use Policy” means the acceptable use policy governing the Platform Services located at databricks.com/legal/aup.“Affiliate” of a party means an entity that controls, is actually or in effect controlled by, or is under common control with such party.“Agreement” means this MCSA, the referenced Schedules, and any accompanying or future Order you enter into under this MCSA.“Authorized User” means employees or agents of Customer or its Affiliates selected by Customer to access and use the Platform Services.“Beta Service” means any feature of the Databricks Services ( that is clearly designated as “beta”, “experimental”, “preview” or similar, that is provided prior to general commercial release, and that Databricks at its sole discretion offers to Customer, and Customer at its sole discretion elects to use."Cloud Environment” means a cloud or other compute or storage infrastructure controlled by a party or by an external user (as may be defined where appropriate by schedule or amendment hereto) according to context and utilized under the Agreement.“Cloud Service Provider” means a cloud service provider on whose platform Databricks directly provides the Platform Services. For clarity, the Databricks Powered Services are not directly provided by Databricks and are not considered Platform Services under this Agreement.“Customer Content” means all data input into or made available by Customer for processing within the Platform Services or Support Services or generated from the Platform or Support Services.“Customer Data” means the data, other than Customer Instructional Input, made available by Customer and its Authorized Users for processing within the Platform Services or Support Services. “Customer Instructional Input” means information other than Customer Data that Customer inputs into the Platform Services to direct how the Platform Services process Customer Data, including without limitation the code and any libraries (including third party libraries) Customer utilizes within the Platform Services.“Customer Results” means any output Customer or its Authorized Users generate from their use of the Platform Services."Databricks Global Code of Conduct” means the Databricks Global Code of Conduct located at databricks.com/global-code-of-conduct.“Databricks Powered Service” means any third-party software or service powered by Databricks, including those at https://www.databricks.com/legal/cloud-provider-directory, that is provided to you under contractual terms between you and a third-party. This Agreement does not amend any term of such contract; the Databricks Powered Services are not considered Databricks Services (and, for the avoidance of doubt, are not considered Platform Services) under the Agreement and Databricks shall have no liability to you relating to your use of the Databricks Powered Services.“Documentation” means the documentation related to the Platform Services located at databricks.com/documentation.“DPA” means the Data Processing Addendum located at databricks.com/legal/dpa.“Effective Date” means the earliest of: the effective date of the initial Order that references this MCSA, the date of last signature of the MCSA, or the date you first access or use any Databricks Services.“Fees” means all amounts payable for Databricks Services.“HIPAA” means the Health Insurance Portability and Accountability Act of 1996, as amended and supplemented from time to time.“Intellectual Property Rights” means all worldwide intellectual property rights available under applicable laws including without limitation rights with respect to patents, copyrights, moral rights, trademarks, trade secrets, know-how, and databases.“Order” means an order form (“Order Form”), online order (including the provisioning of any Databricks Services) or similar agreement for the provision of Databricks Services, entered into by the parties or any of their Affiliates, incorporated by reference into, and governed by, the Agreement. By entering into an Order Form hereunder, an Affiliate agrees to be bound by the terms of this Agreement as if it were an original party hereto.“PCI-DSS” means the Payment Card Industry Data Security Standard.“PHI” means health information regulated by HIPAA or by any similar privacy law governing the use of or access to health information.“Security Addendum” means the Platform Security Addendum located at databricks.com/legal/security-addendum.“Schedule” means any of the schedules referenced herein or otherwise set forth in an Order.“Shared Data” means (i) Customer Content that you electto share with third parties or (ii) data you elect to receive from third parties, under an applicable configuration of the Platform Services.“Support Policy” means the available Support Services plans located at databricks.com/support.“System” means any application, computing or storage device, or network.“Usage Data” means usage data and telemetry collected by Databricks relating to Customer's use of the Platform Services. Usage Data may contain queries entered by an Authorized User but not the results of those queries.“Workspace” means a Platform Services environment.Confidentiality.Confidential Information. “Confidential Information” means any business or technical information disclosed by either party to the other that is designated as confidential at the time of disclosure or that, under the circumstances, a person exercising reasonable business judgment would understand to be confidential or proprietary. Without limiting the foregoing, all non-public elements of the Databricks Services are Databricks’ Confidential Information, Customer Content is Customer’s Confidential Information, and the terms of the Agreement and any information that either party conveys to the other party concerning data security measures, incidents, or findings constitute Confidential Information of both parties. Confidential Information will not include information that the receiving party can demonstrate (a) is or becomes publicly known through no fault of the receiving party, (b) is, when it is supplied, already known to whoever it is disclosed to in circumstances in which they are not prevented from disclosing it to others, (c) is independently obtained by whoever it is disclosed to in circumstances in which they are not prevented from disclosing it to others or (d) was independently developed by the receiving party without use of or reference to the Confidential Information.Confidentiality. A receiving party will not use the disclosing party’s Confidential Information except as permitted under the Agreement or to enforce its rights under the Agreement and will not disclose such Confidential Information to any third party except to those of its employees and/or subcontractors who have a bona fide need to know such Confidential Information for the performance or enforcement of the Agreement; provided that each such employee and/or subcontractor is bound by a written agreement that contains use and disclosure restrictions consistent with the terms set forth in this Section 2.2. Each receiving party will protect the disclosing party’s Confidential Information from unauthorized use and disclosure using efforts equivalent to those that the receiving party ordinarily uses with respect to its own Confidential Information of similar nature and in no event using less than a reasonable standard of care; provided, however, that a party may disclose such Confidential Information as required by applicable laws, subject to the party required to make such disclosure giving reasonable notice to the other party to enable it to contest such order or requirement or limit the scope of such request. The provisions of this Section 2.2 will supersede any non-disclosure agreement by and between the parties (whether entered into before, on or after the Effective Date) that would purport to address the confidentiality and security of Customer Content and such agreement will have no further force or effect with respect to Customer Content.Equitable Relief. Each party acknowledges and agrees that the other party may be irreparably harmed in the event that such party breaches Section 2.2 (Confidentiality), and that monetary damages alone cannot fully compensate the non-breaching party for such harm. Accordingly, each party hereto hereby agrees that the non-breaching party will be entitled to seek injunctive relief to prevent or stop such breach, and to obtain specific enforcement thereof. Any such equitable remedies obtained will be in addition to, and not foreclose, any other remedies that may be available.Intellectual Property.
Ownership of the Databricks Services. Except for the limited licenses expressly set forth in the Agreement, Databricks retains all Intellectual Property Rights and all other proprietary rights related to the Databricks Services. You will not delete or alter the copyright, trademark, or other proprietary rights notices or markings appearing within the Databricks Services as delivered to you. You agree that the Databricks Services are provided on a non-exclusive basis and that no transfer of ownership of Intellectual Property Rights will occur. You further acknowledge and agree that portions of the Databricks Services, including but not limited to the source code and the specific design and structure of individual modules or programs, constitute or contain trade secrets and other Intellectual Property Rights of Databricks and its licensors.Ownership of Customer Content. As between you and Databricks, you retain all ownership or license rights in Customer Content.Usage Data. Notwithstanding anything to the contrary in the Agreement, Databricks may collect and use Usage Data to develop, improve, operate, and support its products and services. Databricks will not share any Usage Data that includes Customer Confidential Information except either (a) to the extent that such Usage Data is anonymized and aggregated such that it does not identify Customer or Customer Confidential Information; or (b) in accordance with Section 2 (Confidentiality) of this Agreement.Feedback. You are under no duty to provide any suggestions, enhancement requests, or other feedback regarding the Databricks Services (“Feedback”). If you choose to offer Feedback to Databricks, you hereby grant Databricks a perpetual, irrevocable, non-exclusive, worldwide, fully-paid, sub-licensable, assignable license to incorporate into the Databricks Services or otherwise use any Feedback Databricks receives from you solely to improve Databricks products and services, provided that such Feedback is used in a manner that is not attributable to you. You also irrevocably waive in favor of Databricks any moral rights which you may have in such Feedback pursuant to applicable copyright law. Databricks acknowledges that any Feedback is provided on an “as-is” basis with no warranties of any kind.Use of the Platform Services.
Access. Databricks will make the Platform Services available to Customer and its Authorized Users in accordance with the terms and conditions of this Agreement, the Documentation, and an applicable Order.Databricks Responsibilities. Services. Databricks is responsible for (a) the operation of the Databricks Cloud Environment; and (b) the Databricks software used to operate the computing resources. Security Measures. Databricks shall implement reasonable administrative, physical, and technical safeguards to protect the security of the Platform Services and the Customer Content as set forth in the Security Addendum (“Security Measures”); and shall, without limiting the foregoing, maintain certification to ISO/IEC 27001:2013 or equivalent/greater standards during the term of this Agreement. Additionally, while it is your responsibility to back up Customer Content, Databricks will, at your reasonable request, provide commercially reasonable assistance with recovery efforts. While Databricks may update the Security Measures, it shall not materially diminish the effectiveness of the Security Measures.Customer Responsibilities. General Responsibilities. You acknowledge and agree that you are responsible for:
ensuring that each Authorized User has their own credentials, protecting those credentials, and not permitting any sharing of credentials;securing any Customer Cloud Environment, and any Customer System;backing up Customer Content; configuring the Platform Services in an appropriate way taking into account the sensitivity of the Customer Content that you choose to process using the Platform Services, including Shared Data; using commercially reasonable efforts to ensure that your Authorized Users review the portions of Documentation relevant to your use of the Platform Services and any security information published by Databricks and referenced therein that is designed to assist you in securing Customer Content;risks associated with all use of the Platform Services by an Authorized User under an Authorized User’s account (including for the payment of Fees related to such use), whether such action was taken by an Authorized User or by another party, and whether or not such action was authorized by an Authorized User, provided that such action was not (1) taken by Databricks or by a party acting under the direction of Databricks, or (2) an action by a third party that Databricks should reasonably have prevented. Use Limits. You will not, and will not permit your Authorized Users to:
violate the Acceptable Use Policy or use the Platform Services other than in accordance with the Documentation;copy, modify, disassemble, decompile, reverse engineer, or attempt to view or discover the source code of the Platform Services, in whole or in part, or permit or authorize a third party to do so, except to the extent such activities are expressly permitted by the Agreement or by law notwithstanding this prohibition;sell, resell, license, sublicense, distribute, rent, lease, or otherwise provide access to the Platform Services to any third party except to the extent explicitly authorized in writing by Databricks;use the Platform Services to develop or offer a service made available to any third party that could reasonably be seen to serve as a substitute for such third party’s possible purchase of any Databricks product or service;transfer or assign any of your rights hereunder except as permitted under Section 12.5 (Assignment); orduring any free trial period granted by Databricks, including during the use of any Beta Service, use the Databricks Services for any purpose other than to evaluate whether to purchase the Databricks Services.Shared Responsibilities. Customer acknowledges that the Platform Services may be implemented in a manner that divides the Platform Services between the Customer Cloud Environment and the Databricks Cloud Environment, and that accordingly each party must undertake certain technical and organizational measures in order to protect the Platform Services and the Customer Content. Permitted Benchmarking. You may perform benchmarks or comparative tests or evaluations (each, a “Benchmark”) of the Platform Services and may disclose the results of the Benchmark other than for Beta Services. If you perform or disclose, or direct or permit any third party to perform or disclose, any Benchmark of any of the Platform Services, you (i) will include in any disclosure, and will disclose to us, all information necessary to replicate such Benchmark, and (ii) agree that we may perform and disclose the results of Benchmarks of your products or services, irrespective of any restrictions on Benchmarks in the terms governing your products or services.Customer Content.Limits on What Customer Content May Contain. You agree that you will not include in Customer Content, or generate any Customer Results that include, any data for which you do not have all rights, power and authority necessary for its collection, use and processing as contemplated by the Agreement.PHI Data. You shall not include in Customer Content any PHI unless (a) you have entered into an Order permitting you to process PHI, and then only with respect to the Workspace(s) or account (if applicable) (together the “PHI Permitted Workspaces”) identified on such Order; and (b) you have entered into a Business Associate Agreement (“BAA”) with Databricks. If you have not entered into a BAA with Databricks or if you provide PHI to Databricks other than through the PHI Permitted Workspaces, Databricks will have no liability under the Agreement relating to PHI notwithstanding anything in the Agreement or in HIPAA or any similar laws to the contrary.Cardholder Data. You shall not include in Customer Content any cardholder data as defined under PCI-DSS (“Cardholder Data”) unless (1) you are processing the Cardholder Data in a PCI Permitted Workspace and configure and operate such Workspace in accordance with the Documentation; and (2) you have entered into an Order that (a) specifies Databricks then-current certification status under PCI-DSS; and (b) explicitly permits you to process Cardholder Data within the Platform Services (including specifying the types and quantities of such data) and then only with respect to the Workspace(s) identified in such Order (the “PCI Permitted Workspaces”). Databricks will have no liability under the Agreement relating to Cardholder Data that is not processed in accordance with the terms of this section notwithstanding anything in the Agreement or in PCI-DSS or any similar regulations to the contrary.Architectures and Services Updates. Databricks provides the Platform Services according to different architectural models (e.g. models where computing resources are deployed into Customer Cloud Environment and models where computing resources are deployed into Databricks Cloud Environments) depending on the specific feature being used by Customer, as further described in the Documentation. Accordingly, Customer acknowledges and agrees that different portions of the Platform Services are and may in the future be subject to changes reflected in the Documentation or terms and conditions that provide for different rights and responsibilities of the parties for their use. Databricks Container ServicesAs part of Databricks Container Services, Databricks may provide a sample stub container file (a “Sample Container”) that you may use to create a custom container file (a “Modified Sample Container”). Databricks grants you a limited, non-exclusive right and license to use and modify the Sample Container to create a Modified Sample Container to use with Databricks Container Services. The Sample Container may contain libraries that are subject to open source licenses. It is your obligation to review and comply with any such licenses prior to your creation of the Modified Sample Container.You may not:
include in a Custom Container any code: (i) for which you do not have the necessary right or license; or (ii) that contains any code that could subject Databricks to any condition that Databricks make any of its source code available or which may impose any other obligation or restriction with respect to Databricks’ Intellectual Property Rights; orattempt to disable or interfere with any technical limitations contained within Databricks Container Services.You grant Databricks a worldwide, non-exclusive royalty free right and license to use, reproduce and make derivative works of the Custom Container solely as necessary to provide Databricks Container Services to Customer.Data Protection. Except with respect to a free trial, the terms of the DPA are hereby incorporated by reference and shall apply to the processing of personal data as described in the DPA.Suspension and Termination of Platform Services.
Suspension. Databricks may temporarily suspend any or all Workspaces at any time: (i) immediately without notice if Databricks reasonably suspects that you have violated your obligations under Section 4.3 (Customer Responsibilities), Section 4.6 (Customer Content), or Section 11 (Compliance with Laws) in a manner that may cause material harm or material risk of harm to Databricks or to any other party; (ii) or if you (or any third party responsible for making payment on your behalf) fail to pay undisputed Fees after receiving notice that you are delinquent in payment.Termination; Workspace Cancellation. Databricks may terminate your use of the Platform Services and any Workspaces and any applicable Order for material breach, including without limitation your breach of Section 4.10(a), that in each case is either not cured within thirty (30) days of notice of such breach or that by its nature is incapable of cure. If the Agreement or any applicable Order is terminated for any reason or upon your written request, Databricks may cancel your Workspaces. Upon termination of the Agreement for any reason you will delete all stored elements of the Platform Services from your Systems.Deletion of Customer Content upon Workspace Cancellation. Databricks will automatically delete all Customer Content contained within a Workspace within thirty (30) days following the cancellation of such Workspace. Monthly Pay-As-You-Go (PAYG) Services. Notwithstanding anything in the Agreement to the contrary, Databricks may suspend or terminate any Platform Services provided on a month-to-month basis with payment based only on Customer’s usage of the Platform Services during the billing month and delete any Customer Content relating to such Workspace that may be stored within the Platform Services or other Databricks’ Systems, upon thirty (30) days’ prior written notice (email sufficient) if Databricks reasonably determines the account is inactive as set forth in the Acceptable Use Policy.Notice. Notwithstanding Section 12.6 (Notice), notice under this Section 4.10 (Suspension; Termination) may be provided by email sent to a person the party providing notice reasonably believes to have responsibility for the other party’s activities under the Agreement.Support Services. Databricks will provide you with the level of Support Services specified in an Order in accordance with the Support Policy. If Support Services are not specified in an Order, your support shall be limited to public Documentation and forums.Warranties; Remedy.Warranties. Each party warrants that it is validly entering into the Agreement and has the legal authority to do so. In addition to the warranties provided by the parties as set forth in any applicable Schedule, Databricks warrants that, during the term of any Order for Platform Services: (a) the Platform Services will function substantially in accordance with the Documentation; and (b) Databricks will employ commercially reasonable efforts in accordance with industry standards to prevent the transmission of malware or malicious code via the Platform Services.Disclaimer. THE WARRANTIES PROVIDED BY DATABRICKS IN SECTION 6.1 (WARRANTIES) ARE EXCLUSIVE AND IN LIEU OF ALL OTHER WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, REGARDING DATABRICKS AND DATABRICKS’ SERVICES PROVIDED HEREUNDER. DATABRICKS AND ITS LICENSORS SPECIFICALLY DISCLAIM ALL IMPLIED WARRANTIES, CONDITIONS AND OTHER TERMS, INCLUDING, WITHOUT LIMITATION, IMPLIED WARRANTIES OF MERCHANTABILITY, SATISFACTORY QUALITY OR FITNESS FOR A PARTICULAR PURPOSE. NOTWITHSTANDING ANYTHING TO THE CONTRARY HEREIN: (a) ANY SERVICES PROVIDED UNDER ANY FREE TRIAL PERIOD ARE PROVIDED “AS-IS” AND WITHOUT WARRANTY OF ANY KIND; (b) WITHOUT LIMITATION, DATABRICKS DOES NOT MAKE ANY WARRANTY OF ACCURACY, COMPLETENESS, TIMELINESS, OR UNINTERRUPTABILITY, OF THE PLATFORM SERVICES; (c), DATABRICKS IS NOT RESPONSIBLE FOR RESULTS OBTAINED FROM THE USE OF THE DATABRICKS SERVICES OR FOR CONCLUSIONS DRAWN FROM SUCH USE; AND (d) EXCEPT AS OTHERWISE STATED IN SECTION 4 (USE OF THE PLATFORM SERVICES), DATABRICKS’ REASONABLE EFFORTS TO RESTORE LOST OR CORRUPTED CUSTOMER INSTRUCTIONAL INPUT DESCRIBED THEREIN SHALL BE DATABRICKS’ SOLE LIABILITY AND YOUR SOLE AND EXCLUSIVE REMEDY IN THE EVENT OF ANY LOSS OR CORRUPTION OF CUSTOMER CONTENT IN CONNECTION WITH THE DATABRICKS SERVICES.Platform Services Warranty Remedy. FOR ANY BREACH OF THE WARRANTIES RELATED TO THE PLATFORM SERVICES PROVIDED BY DATABRICKS IN SECTION 6.1 (WARRANTIES), YOUR EXCLUSIVE REMEDY AND DATABRICKS’ ENTIRE LIABILITY WILL BE THE MATERIAL CORRECTION OF THE DEFICIENT SERVICES THAT CAUSED THE BREACH OF WARRANTY, OR, IF WE CANNOT SUBSTANTIALLY CORRECT THE DEFICIENCY IN A COMMERCIALLY REASONABLE MANNER, DATABRICKS WILL END THE DEFICIENT SERVICES AND REFUND TO YOU THE PORTION OF ANY PREPAID FEES PAID BY YOU TO DATABRICKS APPLICABLE TO THE PERIOD FOLLOWING THE EFFECTIVE DATE OF TERMINATION.Indemnification.
Indemnification by Databricks. Subject to Section 7.5 (Conditions of Indemnification), Databricks will defend Customer against any claim, demand, suit or proceeding made or brought against Customer by a third party (a “Claim Against Customer”)alleging that the Databricks Services as provided to Customer by Databricks or Customer’s use of the Databricks Services in accordance with the Documentation and the Agreement infringes or misappropriates such party’s Intellectual Property Rights (an “IP Claim”), and will indemnify Customer from and against any damages, attorney fees and costs finally awarded against Customer as a result of, or for amounts paid by Customer under a settlement approved by Databricks in writing of, a Claim Against Customer. Notwithstanding the foregoing, Databricks will have no liability for any infringement or misappropriation claim of any kind if such claim arises from: (a) the public open source version of Apache Spark (located at github.com/apache/spark) if the claim of infringement or misappropriation does not allege specifically that the infringement or misappropriation arises from the Platform Services (as opposed to Apache Spark itself); (b) the combination, operation or use of the Databricks Services with equipment, devices, software or data (including without limitation your Confidential Information) not supplied by Databricks if a claim would not have occurred but for such combination, operation or use; or (c) your or an Authorized User’s use of the Databricks Services other than in accordance with the Documentation and the Agreement.Other Remedies. If Databricks receives information about an infringement or misappropriation claim related to a Databricks Service or otherwise becomes aware of a claim that the provision of any of the Databricks Services is unlawful in a particular territory, then Databricks may at its sole option and expense: (a) replace or modify the applicable Databricks Services to make them non-infringing and of substantially equivalent functionality; (b) procure for you the right to continue using the Databricks Services under the terms of the Agreement; or (c) if Databricks is unable to accomplish either (a) or (b) despite using its reasonable efforts, terminate your rights and Databricks’ obligations under the Agreement with respect to such Databricks Services and refund to you any Fees prepaid by you to Databricks for Databricks Services not yet provided.Indemnification by Customer. Subject to Section 7.5 (Conditions of Indemnification), Customer will defend Databricks against any claim, demand, suit or proceeding made or brought against Databricks by a third party (a “Claim Against Databricks”) (a) arising from or related to Customer’s use of the Databricks Services in violation of any applicable laws, the rights of a third party, or the Agreement, or (b) arising from or related to Customer Content or its use with the Databricks Services, (c) alleging that any information and / or materials you provide to Databricks for Databricks to perform Advisory Services as defined in an Advisory Services Schedule (if applicable) (“Customer Materials”) or the use of Customer Materials with the Databricks Services infringes or misappropriates such party’s Intellectual Property Rights, and / or (d) arising from any instructions provided by Customer to Databricks in the creation by Databricks of the Deliverables (as defined in the Advisory Services Schedule (if applicable)), and will indemnify Databricks from and against any damages, attorney fees and costs finally awarded against Databricks as a result of a Claim Against Databricks, or for amounts paid by Databricks under a settlement approved by Customer in writing.Sole Remedy. SUBJECT TO SECTION 7.5 (CONDITIONS OF INDEMNIFICATION) BELOW, THE FOREGOING SECTIONS 7.1 (INDEMNIFICATION BY DATABRICKS) AND 7.2 (OTHER REMEDIES) STATE THE ENTIRE OBLIGATION OF DATABRICKS AND ITS LICENSORS WITH RESPECT TO ANY ALLEGED OR ACTUAL INFRINGEMENT OR MISAPPROPRIATION OF INTELLECTUAL PROPERTY RIGHTS BY THE DATABRICKS SERVICES.Conditions of Indemnification. As a condition to an indemnifying party’s (each, an “Indemnitor”) obligations under this Section 7 (Indemnification), a party seeking indemnification (each, an ”Indemnitee”) will: (a) promptly notify the Indemnitor of the claim for which the Indemnitee is seeking indemnification (but late notice will only relieve Indemnitor of its obligation to indemnify to the extent that it has been prejudiced by the delay); (b) grant the Indemnitor sole control of the defense (including selection of counsel) and settlement of the claim; (c) provide the Indemnitor, at the Indemnitor’s expense, with all assistance, information and authority reasonably required for the defense and settlement of the claim; and (d) preserve and will not waive legal, professional or any other privilege attaching to any of the records, documents, or other information in relation to such claim without prior notification of consent by the Indemnitor. The Indemnitor will not settle any claim in a manner that does not fully discharge the claim against an Indemnitee or that imposes any obligation on, or restricts any right of, an Indemnitee without the Indemnitee’s prior written consent, which may not be unreasonably withheld or delayed. An Indemnitee has the right to retain counsel, at the Indemnitee’s expense, to participate in the defense or settlement of any claim. The Indemnitor will not be liable for any settlement or compromise that an Indemnitee enters into without the Indemnitor’s prior written consent.Limitation of Liability.
EXCEPT WITH RESPECT TO (I) LIABILITY THAT CANNOT BE EXCLUDED OR LIMITED BY APPLICABLE LAWS, (II) LIABILITY ARISING OUT OF FRAUD OR FRAUDULENT MISREPRESENTATION, OR (III) CUSTOMER’S INDEMNIFICATION OBLIGATIONS, NEITHER PARTY WILL HAVE ANY LIABILITY FOR: (A) INDIRECT, INCIDENTAL, SPECIAL, PUNITIVE, OR CONSEQUENTIAL LOSS OR DAMAGES; (B) LOST PROFITS OR REVENUE; (C) LOSS OF GOODWILL; (D) LOSS OF DATA; OR (E) LOSS ARISING FROM INACCURATE OR UNEXPECTED RESULTS ARISING FROM THE USE OF THE DATABRICKS SERVICES, REGARDLESS OF WHETHER SUCH PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH LOSSES OR DAMAGES ARISING.SUBJECT TO SECTIONS 8.1, 8.3, 8.4 AND 8.5, EXCEPT WITH RESPECT TO LIABILITY ARISING OUT OF: (I) PERSONAL INJURY OR DEATH CAUSED BY THE NEGLIGENCE OF A PARTY, ITS EMPLOYEES, OR AGENTS; (II) DATABRICKS’ INDEMNIFICATION OBLIGATIONS FOR AN IP CLAIM; OR (III) CUSTOMER’S INDEMNIFICATION OBLIGATIONS, IN NO EVENT WILL THE AGGREGATE LIABILITY OF EACH PARTY TOGETHER WITH ALL OF ITS AFFILIATES ARISING OUT OF OR RELATED TO THE AGREEMENT EXCEED THE TOTAL AMOUNT PAID BY CUSTOMER AND ITS AFFILIATES FOR THE DATABRICKS SERVICES GIVING RISE TO THE LIABILITY IN THE TWELVE (12) MONTHS PRECEDING THE FIRST INCIDENT OUT OF WHICH THE LIABILITY AROSE (THE “GENERAL CAP”). THE FOREGOING LIMITATION WILL APPLY WHETHER AN ACTION IS IN CONTRACT OR TORT AND REGARDLESS OF THE THEORY OF LIABILITY, BUT WILL NOT LIMIT CUSTOMER’S AND ITS AFFILIATES’ PAYMENT OBLIGATIONS UNDER SECTION 10 (PAYMENT).SUBJECT TO SECTIONS 8.1, 8.4 AND 8.5, DATABRICKS’ AGGREGATE LIABILITY FOR ANY CLAIMS OR DAMAGES, DIRECT OR OTHERWISE, ARISING OUT OF OR IN CONNECTION WITH DATABRICKS’ BREACH OF ITS CONFIDENTIALITY OBLIGATIONS (SECTION 2.2) OR, WITH RESPECT TO THE PROVISION BY DATABRICKS OF THE PLATFORM SERVICES (IF APPLICABLE), THE DATA PROTECTION AND SECURITY OBLIGATIONS SET FORTH IN THIS AGREEMENT AND THE DPA, WHERE SUCH BREACH RESULTS IN UNAUTHORIZED DISCLOSURE OF CUSTOMER CONTENT, EXCEPT TO THE EXTENT SUCH CLAIMS OR DAMAGES ARE CAUSED BY DATABRICKS’ GROSS NEGLIGENCE OR WILLFUL MISCONDUCT, SHALL BE LIMITED TO TWO (2) TIMES THE TOTAL AMOUNT PAID BY CUSTOMER AND ITS AFFILIATES FOR THE DATABRICKS SERVICES GIVING RISE TO THE LIABILITY IN THE TWELVE (12) MONTHS PRECEDING THE FIRST INCIDENT OUT OF WHICH THE LIABILITY AROSE (“SUPERCAP”).IN NO EVENT SHALL DATABRICKS BE LIABLE FOR THE SAME EVENT UNDER BOTH THE GENERAL CAP AND THE SUPERCAP. SIMILARLY, THOSE CAPS SHALL NOT BE CUMULATIVE; IF THERE ARE ONE OR MORE CLAIMS SUBJECT TO EACH OF THOSE CAPS, THE MAXIMUM TOTAL LIABILITY FOR ALL CLAIMS IN THE AGGREGATE SHALL NOT EXCEED THE SUPERCAP.NOTWITHSTANDING ANYTHING CONTAINED ABOVE, DATABRICKS' LIABILITY RELATING TO BETA SERVICES OR ANY DATABRICKS SERVICES PROVIDED FREE OF CHARGE, INCLUDING ANY DATABRICKS SERVICES PROVIDED DURING A FREE TRIAL PERIOD, WILL BE LIMITED TO FIVE THOUSAND US DOLLARS (USD $5,000).TermTerm of Agreement. The Agreement will become effective on the Effective Date and will continue in full force and effect until terminated by either party pursuant to this Section 9 (Term). The Agreement may be terminated (i) by either party on thirty (30) days’ prior written notice if (a) there are no operative Orders outstanding or (b) the other party is in material breach of the Agreement and the breaching party fails to cure the breach prior to the end of the notice period; or (ii) by Databricks upon thirty (30) days’ prior written notice following your receipt of a notice that you are delinquent in the payment of undisputed Fees. If the Agreement terminates pursuant to the prior sentence due to Databricks’ material breach, Databricks will refund to you that portion of any prepayments made to Databricks related to Databricks Services not yet provided. Either party can immediately terminate the Agreement if the other becomes insolvent, makes an assignment for the benefit of its creditors, has a receiver, examiner, or administrator of its undertaking of the whole or a substantial part of its assets appointed, or an order is made, or an effective resolution is passed, for its administration, examinership, receivership, liquidation, winding-up or other similar process, or has any distress, execution or other process levied or enforced against the whole or a substantial part of its assets (which is not discharged, paid out, withdrawn or removed within 30 days), or is subject to any proceedings which are equivalent or substantially similar to any of the foregoing under any applicable jurisdiction, or ceases to conduct business or threatens to do so.Term of Orders. The Term of an Order will be as specified in the Order.Survival. All provisions of the Agreement that by their nature should survive termination will so survive.Payment. Unless your usage of the Databricks Services is being paid for by a third party under contract with Databricks, you will pay all Fees specified in the applicable Order. With respect to direct Order, except as otherwise specified therein: (a) all Fees owed to Databricks will be paid in U.S. Dollars; (b) invoiced payments will be due within 30 days of the date of your receipt of each invoice; (c) Fees for all prepaid committed Databricks Services will be invoiced in full upon execution of the applicable Order; and (d) all excess usage will be invoiced monthly in arrears. With respect to an Order entered into with a reseller, payment terms will be specified on such Order, provided that should you fail to pay Fees when due to a Databricks-authorized reseller, Databricks may seek payment directly from you. All past due payments, except to the extent reasonably disputed, will accrue interest at the highest rate allowed under applicable laws but in no event more than one and one-half percent (1.5%) per month. You will be solely responsible for payment of any applicable sales, value added or use taxes, or similar government fees or taxes.Compliance with Laws.By Databricks Generally. Databricks will provide the Databricks Services in accordance with its obligations under laws and government regulations applicable to Databricks’ provision of the Databricks Services to its customers generally, including, without limitation those related to data protection and data privacy, irrespective of Customer’s particular use of the services.By Customer Generally. You represent and warrant to Databricks that your use of Databricks Services will comply with all applicable laws and government regulations, including without limitation those related to data protection and data privacy. Export Controls; Trade Sanctions. The Databricks Services may be subject to export controls and trade sanctions laws of the United States and other jurisdictions. Customer acknowledges and agrees that it will comply with all applicable export controls and trade sanctions laws, regulations and/or any other relevant restrictions in Customer’s use of the Databricks Services, including that you will not permit access to or use of any Databricks Services in any country where such access or use is subject to a trade embargo or prohibition, and that you will not use Databricks Services in support of any controlled technology, industry, or goods or services, or any other restricted use, without having a valid governmental license, authority, or permission to engage in such conduct. Each party further represents that it (and with respect to Customer, each Authorized User and / or Affiliate accessing the Databricks Services) is not named on any governmental or quasi-governmental denied party or debarment list relevant to this Agreement, and is not owned directly or indirectly by persons whose aggregated interest in such party is 50% or more and who are named on any such list(s). Business Practices; Code of Conduct. Databricks maintains a set of business practice principles and policies in the Databricks Global Code of Conduct, which employees are required to follow. Databricks will abide by these principles and policies in the conduct of all business for Customer and expects your use of any Databricks Services to be conducted utilizing principles of business ethics and social responsibility and, with respect to any Platform Services, in accordance with Databricks’ Acceptable Use Policy and the applicable Platform Services terms set forth in the Agreement.General.
Governing Law and Venue. The governing law and exclusive venue applicable to any lawsuit or other dispute arising in connection with the Agreement will be determined by the location of Customer’s principal place of business (“Domicile”), as follows:
Customer’s DomicileGoverning LawVenue(courts with exclusive jurisdiction)CaliforniaCaliforniaSan Francisco(state and U.S. federal courts)Americas (except California and Canada); Middle East; AfricaDelawareDelaware(state and U.S. federal courts)CanadaOntarioTorontoUnited KingdomEngland & WalesLondonEurope (including Turkey)IrelandDublinPacific & AsiaSingaporeSingaporeAustralia and New ZealandAustraliaVictoriaThe parties hereby irrevocably consent to the personal jurisdiction and venue of the courts in the venues shown above. Unless prohibited by governing law or venue, each party irrevocably agrees to waive jury trial. In all cases, the application of law will be without regard to, or application of, conflict of law rules or principles, and the United Nations Convention on Contracts for the International Sale of Goods will not apply.Insurance Coverage.Databricks will maintain commercially appropriate insurance coverage given the nature of the Databricks Services and Databricks’ obligations under the Agreement. Such insurance will be in an industry standard form with licensed insurance carriers with A.M. Best ratings of A-IX or better, and will include commercially appropriate cyber liability insurance coverage. Upon request, Databricks will provide Customer with certificates of insurance evidencing such coverage.Entire Agreement, Construction, Amendment and Execution. The Agreement is the complete and exclusive understanding and agreement between the parties regarding its subject matter, provided that to the extent Customer uses any Databricks Services subject to Schedules not included in the Agreement, the relevant Schedule in effect at the time of first use at databricks.com/legal/mcsa shall be deemed to govern use of such Databricks Services unless the parties agree otherwise in writing and any reference to a term in such Schedule shall be interpreted accordingly. Databricks may change and update the Platform Services, in which case Databricks may update the Documentation. To the extent any provision in an Order clearly conflicts with a provision of this MCSA or a provision of an earlier Order, the provision in the new Order will be binding and the conflicting provision in this MCSA or in the earlier Order will be deemed modified solely to the extent reasonably necessary to eliminate the conflict and solely with respect to the new Order (unless expressly intended to permanently amend the Agreement including any Schedule). Customer’s Affiliates may receive the Databricks Services under this Agreement as Authorized Users, however in the event that a Customer Affiliate wishes to execute its own Order subject to the terms of this Agreement then Customer agrees to remain jointly and severally liable for such use. If any provision of the Agreement is held to be unenforceable or invalid, that provision will be enforced to the maximum extent possible and the other provisions will remain in full force and effect. The headings in the Agreement are solely for convenience and will not be taken into consideration in interpretation of the Agreement. Any translation of the Agreement or an Order that is provided as a courtesy shall not be legally binding and the English language version will always prevail. Each party acknowledges and agrees that it has adequate sophistication, including legal representation, fully to review and understand the Agreement; therefore, in interpretation of the Agreement with respect to any drafting ambiguities that may be identified or alleged, no presumption will be given in favor of the non-drafting party. The Agreement may not be modified or amended except by mutual written agreement of the parties. Without limiting the foregoing, no Customer purchase order will be deemed to modify an Order or the Agreement unless expressly pre-authorized in writing by Databricks. The Agreement may be executed in two or more counterparts, each of which will be deemed an original and all of which, taken together, will constitute one and the same instrument. A party’s electronic signature or transmission of any document by electronic means will be deemed to bind such party as if signed and transmitted in physical form.Publicity. Customer consents to Databricks’ use of Customer's name and logo for public identification as a customer, along with general descriptions of any non-confidential matters Databricks has handled for Customer in promotional marketing materials and press releases. In addition, upon request, Customer consents to participating in a case study regarding its experiences with the Databricks Services ("Case Study"), and inclusion of the Case Study in promotional marketing materials and press releases.Assignment. No assignment, novation or transfer of a party’s rights and obligations under the Agreement (“Assignment”) is permitted except with the prior written approval of the other party, which will not be unreasonably withheld. Notwithstanding the foregoing, either party may freely make an Assignment to a successor in interest upon a change of control; if such Assignment is to a direct competitor of the other party or would cause the other party to become in violation of applicable laws that is not reasonably addressable, such other party may terminate the Agreement upon written notice.Notice. Any required notice under the Agreement will be deemed given when received by letter delivered by nationally recognized overnight delivery service or recorded prepaid mail. Unless notified in writing of a change of address, you will send any required notice to Databricks, Inc., 160 Spear Street, Suite 1300, San Francisco, CA 94105, USA, attention: Legal Department, or to the alternative Databricks Affiliate (if any) identified in an applicable Order, and Databricks will send any required notice to you directed to the most recent address you have provided to Databricks for such notice.Force Majeure. Neither party will be liable or responsible to the other party nor be deemed to have defaulted under or breached the Agreement for any failure or delay in fulfilling or performing any term of the Agreement (except for any obligations to make payments to the other party), when and to the extent such failure or delay is caused by or results from acts beyond the impacted party’s (“Impacted Party”) reasonable control, including without limitation the following force majeure events (“Force Majeure Event(s)“): (a) acts of God, (b) acts of government, including any changes in law or regulations, (c) acts or omissions of third parties, (d) flood, fire, earthquakes, civil unrest, wars, acts of terror, pandemics, or strikes or other actions taken by labor organizations, (e) computer, telecommunications, the Internet, Internet service provider or hosting facility failures or delays involving hardware, software or power systems not within the Impacted Party’s possession or reasonable control, (f) network intrusions or denial of service attacks, or (g) any other cause, whether similar or dissimilar to any of the foregoing, that is beyond the Impacted Party’s reasonable control.Last Updated December 1, 2022. For earlier versions, please send a request to [email protected] (with “TOS Request” in the subject).MCSA_OnlineStandard_ENG_v.3.0_20221201ProductPlatform 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.
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https://www.databricks.com/dataaisummit/speaker/prashanth-babu | Prashanth Babu - 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 ExperiencePricingPrashanth BabuLead Product Specialist at DatabricksBack to speakersPrashanth is a Lead Product Specialist Solutions Architect at Databricks. He has more than a decade experience working on Big Data. Prior to that for a decade, he was a Java architect for a product. He has been using Spark since 2014 and joined Databricks in Sep 2018. He focuses on all things Data Engineering working closely with both the Product Management and the (EMEA) Field Engineering teams. Also leads EMEA Delta and Performance SME at Databricks and has been working with many enterprises advising them on Databricks Lakehouse best practices and guiding them expedite build, productionize and deploy their pipelines at scale.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/austin-ford/# | Austin Ford - 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 ExperiencePricingAustin FordStaff Product Manager at DatabricksBack to speakersAustin is a product leader and former mathematician from Columbus, Mississippi. He is currently a Sr. Product Manager at Databricks in San Francisco and is responsible for the Databricks Notebook and the data science development experience in 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/dataaisummit/speaker/ananya-ghosh | Ananya Ghosh - 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 ExperiencePricingAnanya GhoshBusiness Application Owner for Databricks at Nationwide InsuranceBack to speakersAnanya Ghosh has over 15 years of experience in DW/DI/BI tools and technologies. Currently she plays the role of Business Application Owner for Capture & Curate platforms including Databricks. Prior to that, she worked as a Databricks Data Engineer and Workspace Administrator for about 2 yrs.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/esg | ESG Performance Analytics - 2021 | 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.
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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 AcceleratorAnalyze ESG PerformancePre-built code, sample data and step-by-step instructions ready to go in a Databricks notebookGet startedTake a quantitative and AI-driven view into sustainability performanceBetter understand and quantify the sustainability and societal impact of investment in a company, or embed better ESG in your own organization with these two Solution Accelerators:Analyze ESG performance
Understand and quantify the sustainability and societal impact of any investment or businessOperationalize ESG in your organization
Embed ESG into your company's strategy to adapt and optimize your operational modelGet the notebookResourcesBlogLearn 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.
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https://www.databricks.com/dataaisummit/speaker/jackie-brosamer/# | Jackie Brosamer - 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 ExperiencePricingJackie BrosamerDirector of Software Engineering at BlockBack 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/timothy-ahrens | Timothy Ahrens - 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 ExperiencePricingTimothy AhrensMessaging Design Division Chief at Department of StateBack to speakersTim Ahrens is a civil service employee with the U.S. Department of State and currently serves as the Division Chief for the Information Resource Management Operations Messaging Systems Office, Messaging Design Division. As MD Division Chief, he leads business process re-engineering, design, development, agile delivery, and tier 3 operational support for five enterprise products: SMART, eRecords, FOIA technology stack, CDS, and the CfA Data.State technology 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/pricing | Pricing - 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 ExperiencePricingPricingConference
Full Conference PassThe full conference pass includes keynotes, Expo Hall, breakout sessions, networking events and on-demand access.
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$0 IN-PERSONChoose 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 pricingDon’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/kr/solutions/industries/manufacturing-industry-solutions | 제조 산업 솔루션 – 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 NOW제조업용 레이크하우스 살펴보기제조업체의 데이터 사용 방식에 맞게 구축된 전용 데이터 플랫폼에서 비용 절감, 생산성 향상 및 데이터 에코시스템의 통합 실현가입하기문의TCO 절감, 성능 향상, 확장성 강화제조업용 레이크하우스기본 내장된 공유 기능과 거버넌스로 모든 데이터, 분석 및 AI 워크로드를 통합하여 내부 및 외부 팀이 필요한 데이터를 적시에 활용하도록 할 수 있습니다.전체 가치 사슬에 미치는 영향고객 참여고객에게 정확한 결과와 원활한 경험 제공고객, 운영 및 자산을 한 곳에서 완벽하게 파악할 수 있도록 지원하므로 제품 수명 주기 전반에서 최고의 가동 시간, 서비스 품질 및 경제적 가치를 제공하고 맞춤형 고객 결과, 선제적인 현장 서비스, 차별화된 미션 크리티컬 솔루션을 제공할 수 있습니다.운영 효율성직원 생산성제품 혁신제조 솔루션 및 파트너제조업체를 위한 최고의 데이터 분석 및 AI 솔루션 제공Databricks 솔루션 액셀러레이터는 모든 기능을 갖춘 노트북과 모범 사례를 포함한 전용 가이드를 통해 제조 부문의 성과 실현을 가속화합니다. 디지털 트윈, 장비의 종합적인 효율, 예측 등의 사용 사례에서 발견, 설계, 개발, 테스트 시간을 단축할 수 있습니다.설비 종합 효율 및 KPI 모니터링확장 가능한 고성능의 엔드투엔드 장비 모니터링 수행
센서/IoT 장치에서 다양 한 형식의 데이터를 점진적으로 수집하여 처리하고 KPI 및 지표를 계산하고 표시하여 가치 있는 인사이트를 얻습니다.시작하기부품 수준 예측간소화된 제조를 위해 부품 수준에서 수요 예측
집계 수준이 아닌 부품 수준에서 수요 예측을 수행하여 공급망 중단을 최소화하고 판매를 늘립니다.시작하기디지털 트윈운영 효율성 향상 및 의사 결정 개선
실제 데이터를 실시간으로 처리하고, 대규모로 통계를 계산하여 여러 다운스트림 애플리케이션에 제공하고 이를 바탕으로 데이터 기반 의사 결정을 수행하여 공장 운영을 최적화합니다.시작하기리테일 액셀러레이터 둘러보기Databricks는 주요 컨설팅 기업과 협력하여 혁신적인 업종별 솔루션을 구축했습니다. Databricks Brickbuilder Solutions는 비용을 절감하고 데이터에서 더 많은 가치를 창출하는 데 도움을 줍니다. 수십 년 간 축적된 업계 경험을 노하우 삼아 Databricks 레이크하우스 플랫폼을 기반으로 개발한 Brickbuilder Solutions는 고객의 요구 사항을 정확히 해결해줍니다.지능적 제조 분석 및 AI를 통해 데이터를 활용하고 상호 운용성을 촉진하며 향상된 인사이트를 대규모로 제공합니다.자세히품질 검사기컴퓨터 비전으로 품질 관리를 자동화하여 결함, 이물질, 이상치 또는 잘못된 설정을 감지합니다.자세히예측 가능한 공급 위험 관리주문 흐름 및 공급업체 성과에 대한 n계층 가시성을 확보하여 효율성을 높이고 예외 사 항을 관리하며 복원력을 강화합니다.자세히모든 파트너 솔루션 보기"Databricks 레이크하우스는 조직 전반의 데이터 액세스에 대한 진입 장벽을 낮춰 전 세계에서 가장 혁신적이고 신뢰할 수 있는 전기 자동차를 제조할 수 있도록 지원합니다."
– Wassym Bensaid, Rivian 소프트웨어 개발 부문 부사장
"지난 수년 동안 Databricks는 활용의 폭이 상당히 넓어졌습니다. Databricks를 빅데이터 및 AI 플랫폼으로 사용하기 시작했지만, 그 범위가 더욱 확장되었습니다. 이제 완전히 다른 분야의 시민 엔지니어와 데이터 사이언티스트들이 이 플랫폼을 현대적인 비즈니스 인텔리전스 도구로 활용해 더욱 현명한 비즈니스 결정을 내리고 있습니다."
—Daniel Jeavons, 고급 분석 CoE 대표 이사, Shell
"Databricks 플랫폼은 엔진 가용성에 대한 위험을 최소화하고 예비 부품의 리드 타임을 단축하며 재고 회전의 효율성을 높일 수 있게 해주었습니다. 그리고 그 덕분에 항공 업계를 선도하는 PBH(Power-by-the-Hour) 유지보수 프로그램인 TotalCare를 제공할 수 있게 되었습니다."
— Stuart Hughes, Rolls-Royce Civil Aerospace 최고 정보 및 디지털 책임자,
리소스eBookERP 데이터 활용웨비나데이터 + AI를 활용하여 제조기업의 예측적 유지관리 개선eBook지능적 제조를 촉진하는 네 가지 요인시작할 준비가 되셨나요?귀사의 비즈니스 목표를 자세히 알아보고 서비스 팀에서 귀사의 성공을 도울 방법을 함께 알아보고자 합니다.Databricks 무료로 시작하기문의제품플랫폼 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
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https://www.databricks.com/glossary/spark-tuning | What is Spark Tuning?PlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
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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 NOWSpark TuningAll>Spark TuningTry Databricks for freeGet StartedWhat is Spark Performance Tuning?Spark Performance Tuning refers to the process of adjusting settings to record for memory, cores, and instances used by the system. This process guarantees that the Spark has a flawless performance and also prevents bottlenecking of resources in Spark. What is Data Serialization?In order, to reduce memory usage you might have to store spark RDDs in serialized form. Data serialization also determines a good network performance. You will be able to obtain good results in Spark performance by:Terminating those jobs that run long.Ensuring that jobs are running on a precise execution engine.Using all resources in an efficiently.Enhancing the system’s performance timeSpark supports two serialization libraries, as follows:Java SerializationKryo SerializationWhat is Memory Tuning?While tuning memory usage, there are three aspects that stand out:The entire dataset has to fit in memory, consideration of memory used by your objects is the must.By having an increased high turnover of objects, the overhead of garbage collection becomes a necessity.You’ll have to take into account the cost of accessing those objects.What is Data Structure Tuning?One option to reduce memory consumption is by staying away from java features that could overhead. Here are a few ways to do this:In case the RAM size is less than 32 GB, the JVM flag should be set to –xx:+ UseCompressedOops. This operation will build a pointer of four bytes instead of eight.Nested structures can be dodged by using several small objects as well as pointers.Instead of using strings for keys you could use numeric IDs and enumerated objectsWhat is Garbage Collection Tuning?In order to avoid the large “churn” related to the RDDs that have been previously stored by the program, java will dismiss old objects in order to create space for new ones. However, by using data structures that feature fewer objects the cost is greatly reduced. One such example would be the employment an array of Ints instead of a linked list. Alternatively, you could use objects in the serialized form, so you will only have a single object for each RDD partition.What is Memory Management?An efficient memory use is essential to good performance. Spark uses memory mainly for storage and execution. Storage memory is used to cache data that will be reused later. On the other hand, execution memory is used for computation in shuffles, sorts, joins, and aggregations. Memory contention poses three challenges for Apache Spark:How to arbitrate memory between execution and storage?How to arbitrate memory across tasks running simultaneously?How to arbitrate memory across operators running within the same task?Instead of avoiding statically reserving memory in advance, you could deal with memory contention when it arises by forcing members to spill.Additional Resources8 Steps for a Developer to Learn Apache Spark with Delta LakeThe Data Engineer's Guide to Apache Spark and Delta LakeComparing Apache Spark and DatabricksBack 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
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https://www.databricks.com/advisory-services-schedule | Advisory Services Schedule | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
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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 NOWLegalTermsDatabricks Master Cloud Services AgreementAdvisory ServicesTraining ServicesUS Public Sector ServicesExternal User TermsWebsite Terms of UseCommunity Edition Terms of ServiceAcceptable Use PolicyPrivacyPrivacy NoticeCookie NoticeApplicant Privacy NoticeDatabricks SubprocessorsPrivacy FAQsDatabricks Data Processing AddendumAmendment to Data Processing AddendumSecurityDatabricks SecuritySecurity AddendumLegal Compliance and EthicsLegal Compliance & EthicsCode of ConductThird Party Code of ConductModern Slavery StatementFrance Pay Equity ReportSubscribe to UpdatesAdvisory Services ScheduleThis Schedule sets forth terms related to the Advisory Services and is incorporated as part of the Master Cloud Services Agreement ("MCSA"). The MCSA and this Schedule, together with any other Schedules that reference or are otherwise incorporated into the MCSA, and any accompanying or future Order you enter into with Databricks issued under the MCSA, comprise the Agreement. This Schedule will co-terminate with the MCSA. Other Schedules do not apply to the services ordered under this Schedule unless expressly referenced as being applicable. Capitalized terms used but not defined in this Schedule have the meaning assigned to them in the MCSA.Additional Definitions.“Customer Materials” means the information and materials you provide to Databricks for Databricks to perform the Advisory Services.Advisory Services.Generally. Subject to the Order, Databricks will provide Advisory Services to facilitate your use of the Databricks platform. Unless otherwise agreed by the parties, Advisory Services will expire one year after the Start Date indicated on the Order and will be booked on the basis of 8-hour service days.Intellectual Property.License. Upon your payment of all Fees under an applicable Order, Databricks grants you a non-exclusive, perpetual, fully paid-up, royalty-free license to use, copy, modify, or create derivative works based on any Advisory Services work product delivered by Databricks to you under the Order (the “Deliverables”). If and to the extent Databricks incorporates any Databricks Materials (as defined below) into the Deliverables, Databricks grants to you a non-exclusive, perpetual, fully paid-up, royalty-free license to use, copy, modify or create derivative works based on such Databricks Materials, solely as incorporated into the Deliverables and solely for your internal business use as reasonably necessary to use the Deliverables for their intended purposes. For the avoidance of doubt, no part of the Platform Services will be deemed to be incorporated into the Deliverables.Databricks Materials. Subject to your rights in your Confidential Information, Databricks will exclusively own all rights, title and interest in and to: (i) the Deliverables; and (ii) any software programs, tools, utilities, processes, inventions, devices, methodologies, specifications, documentation, techniques, training materials, and other materials of any kind used or developed by Databricks or its personnel in connection with performing the Advisory Services, or any other Databricks Services (collectively “Databricks Materials”), including all Intellectual Property Rights in any of the foregoing.No Maintenance. Unless otherwise set forth in an Order the Deliverables are not subject to any maintenance, support or updates after the termination of the Order.Requirements; Limitations. Databricks will provide the Advisory Services remotely or at a mutually agreed location. While on Customer’s premises, Databricks will adhere to reasonable policies provided by Customer in writing in advance. For the avoidance of doubt, no such policies will be deemed to modify the terms of the Agreement.Use of Workspace during Performance of Advisory Services. You may be required to use a Workspace in order to receive Advisory Services. Your use of the Workspace constitutes acceptance of the Platform Services terms of the MCSA unless the Workspace is provided as part of a Databricks Powered Service.Your Obligations; Customer Materials. Your Responsibilities. You:
are responsible for taking reasonable steps at all times to maintain the security, protection and backup of all Customer Materials, including within the Platform Services and any Customer Systems;acknowledge that: (i) Databricks does not provide data backup services; and that (ii) Databricks is not responsible for any loss, destruction, alteration, unauthorized disclosure or corruption of Customer Materials not caused by the gross negligence or willful misconduct of Databricks or any third party under the control of Databricks;agree not to provide Databricks with access to more data than is reasonably necessary to permit Databricks to perform the Advisory Services; andacknowledge that successful delivery of the Advisory Services depends on your full and timely cooperation. You agree to make available any reasonably requested personnel and/or information in a timely manner to allow Databricks to perform such services.Restrictions on Use. You will not:
copy, modify, disassemble, decompile, reverse engineer, or attempt to view or discover the source code of any Deliverables provided to you in object code, in whole or in part, or permit or authorize a third party to do so, except to the extent such activities are expressly permitted by the Agreement or by law notwithstanding this prohibition;use the Databricks Services to develop or offer a service made available to any third party that could reasonably be seen to serve as a substitute for such third party's possible purchase of any Databricks product or service; ortransfer or assign any of your rights hereunder except as permitted under Section 12.5 (Assignment) of the MCSA.Customer Materials. You represent and warrant to Databricks that Customer Materials will not contain:
any data for which you do not have all rights, power and authority necessary for its collection, use and processing as contemplated by the Agreement; orexcept as otherwise specified in an Order, any (x) bank, credit card or other financial account numbers or login credentials, (y) social security, tax, driver’s license or other government-issued identification numbers, or (z) health information identifiable to a particular individual.Data Protection. Databricks will maintain appropriate administrative, physical, and technical safeguards according to ISO/IEC 27001:2013 (the “ISMS Standard”) for protection of the security and confidentiality of Customer Materials under Databricks’ control. Unless specified otherwise in an Order, Databricks engages in the performance of Advisory Services with the expectation that Customer is not engaging Databricks for the purpose of having Databricks act as a data processor for Customer. Nevertheless, except with respect to free Advisory Services, unless you have entered into the DPA, the terms of the DPA are hereby incorporated by reference and will apply to the extent Databricks is deemed to act as Customer’s data processor during the performance of Advisory Services when the Customer Materials include Personal Data, as defined in the DPA. This Schedule and the DPA do not govern the protection of Customer Content and Databricks does not act as a data processor with respect to any data processed by or within a Databricks Powered Service.Expenses. You agree to reimburse Databricks for reasonable travel and lodging expenses actually incurred by Databricks.Warranties; Disclaimer.Warranties. Databricks warrants that the Advisory Services will be provided in a professional and workmanlike manner consistent with industry standards. You must notify Databricks of any warranty deficiencies within ninety (90) days from performance of the deficient Advisory Services. Unless set forth in an Order Databricks makes no guarantee as to whether the Advisory Services will be completed within any specific time frame.Disclaimer. THE WARRANTIES IN SECTION 6.1 (WARRANTIES) ARE EXCLUSIVE AND IN LIEU OF ALL OTHER WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, REGARDING THE DATABRICKS’ SERVICES PROVIDED HEREUNDER. DATABRICKS SPECIFICALLY DISCLAIMS ALL IMPLIED WARRANTIES, CONDITIONS AND OTHER TERMS INCLUDING, WITHOUT LIMITATION, IMPLIED WARRANTIES, CONDITIONS AND OTHER TERMS OF MERCHANTABILITY, SATISFACTORY QUALITY OR FITNESS FOR A PARTICULAR PURPOSE WITH RESPECT TO ANY OF THE FOREGOING. NOTWITHSTANDING ANYTHING TO THE CONTRARY HEREIN: (i) SERVICES PROVIDED UNDER ANY FREE TRIAL PERIOD ARE PROVIDED "AS-IS" AND WITHOUT WARRANTY OF ANY KIND BY DATABRICKS; (ii) WITHOUT LIMITATION, DATABRICKS DOES NOT MAKE ANY WARRANTY OF ACCURACY, COMPLETENESS, TIMELINESS, OR UNINTERRUPTABILITY, OF THE DATABRICKS SERVICES; AND (iii) DATABRICKS IS NOT RESPONSIBLE FOR RESULTS OBTAINED FROM THE USE OF THE DATABRICKS SERVICES OR FOR CONCLUSIONS DRAWN FROM SUCH USE.Exclusive Remedy. FOR ANY BREACH OF THE WARRANTY AT SECTION 6.1 (WARRANTIES), YOUR EXCLUSIVE REMEDY AND DATABRICKS’ ENTIRE LIABILITY WILL BE THE RE-PERFORMANCE OF THE DEFICIENT SERVICES, OR, IF DATABRICKS CANNOT SUBSTANTIALLY CORRECT THE DEFICIENCY IN A COMMERCIALLY REASONABLE MANNER, DATABRICKS WILL END THE DEFICIENT SERVICES AND REFUND TO YOU THE PORTION OF ANY PREPAID FEES PAID BY YOU TO DATABRICKS APPLICABLE TO THE PERIOD FOLLOWING THE COMMENCEMENT OF THE DEFICIENCY.Last Updated August 12, 2022. For earlier versions, please send a request to [email protected] (with “TOS Request” in the subject).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
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https://www.databricks.com/dataaisummit/speaker/gaurav-saraf/# | Gaurav Saraf - 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 ExperiencePricingGaurav SarafProduct 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/customers/hotels-com | Hotels com - 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 STORYPersonalized hotel recommendations with deep learning INDUSTRY: Travel and hospitality SOLUTION: Customer retention,recommendation engines PLATFORM USE CASE: Data science,machine learning,ETL CLOUD: AWS“Agility and flexibility were critical for us to successfully support our data science and engineering goals. Moving to Databricks’ Unified Analytics Platform to run 100% of our workflows has been a huge boost for our business and our customers.”
—Matt Fryer VP, Chief Data Science Officer, Hotels.comHotels.com is a premier website for booking accommodations online with 90 websites in 41 languages, listing over 325,000 hotels in approximately 19,000 locations. Their travel booking app has been downloaded over 70 million times helping global travelers find the perfect place to stay.The ChallengesHotels.com hosts millions of photos for the 325,000+ hotels on their website. Every day thousands of new photos are uploaded by properties and customers alike. These photos need to be rapidly analyzed to avoid duplicative and low-quality images and then classified (e.g. kitchen, pool, gym) so they can be logically sorted. Finally, as customers search the site, hotel recommendations need to be personalized to help customers find the perfect hotel for their needs. Achieving this requires massive compute power and advanced analytics.Leverage machine learning to drive consumer experience: Massive volume of image files corresponding to each property listing included duplicates and lacked organization for ranking and classification. Needed to build in real-time scoring and become more efficient at deploying machine learning/deep learning models into production.Build a more robust and faster data pipeline: On premise Hadoop cluster using SQL and SAS to do data science at scale was slow and limiting – taking 2 hours to process the data pipeline on only 10% of the data.Increase customer conversions: Being able to understand customer trends in real-time to develop strategies to drive conversion and lifetime value.The SolutionDatabricks has helped Hotels.com to realize its goal of becoming “data science focused” so that they can anticipate customer behavior and provide a more optimized user experience.Cluster Management: Able to scale volume of data significantly without adding infrastructure complexity.Interactive Workspace: Foster a culture of collaboration among data science teams within Hotels.com as well as other business units within Expedia.Databricks Runtime: Increase processing performance of streaming data even at scale.“Agility and flexibility were critical for us to successfully support our data science and engineering goals. Moving to Databricks’ Unified Analytics Platform to run 100% of our workflows has been a huge boost for our business and our customers.”— Matt Fryer VP, Chief Data Science Officer, Hotels.comThe ResultsAccelerate ETL at scale: Able to increase the volume of data processed by 20x without impacting performance.Optimized user experience: Highly accurate and effective display of images within the context of property searches by customers.Increased sales efficiency: Providing the right hotel with the right images based on searches has resulted in higher conversions.Related ContentWhitepapersSlides Keynote SlidesReady 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.
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https://www.databricks.com/it/try-databricks?itm_data=SiteWide-Footer-Trial | Prova Databricks gratis | DatabricksProva Databricks gratisProva la piattaforma Databricks completa per 14 giorni su AWS, Microsoft Azure o Google Cloud.Semplifica l’acquisizione dei dati e automatizza i processi ETLAcquisisci i dati da centinaia di sorgenti. Usa un semplice approccio dichiarativo per costruire le pipeline di dati.Collabora nella lingua che preferisciScrivi codice in Python, R, Scala e SQL con co-creazione, gestione automatica delle versioni, integrazioni Git e controllo degli accessi per ruoli (RBAC).Rapporto prezzo/prestazioni 12 volte migliore rispetto ai data warehouse in cloudScopri perché oltre 7.000 clienti in tutto il mondo si affidano a Databricks per tutti i loro carichi di lavoro, dalla BI all’AI.Crea il tuo account Databricks1/2NomeCognomeE-mail aziendaleAziendaTitolo professionaleTelefono (Optional)Si prega di selezionareNazioneContinuaInformativa sulla privacy (aggiornata)Condizioni d'usoLe vostre scelte sulla privacyI vostri diritti di privacy in California |
https://www.databricks.com/glossary/hive-date-function | What is a Hive Date Function?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 NOWHive Date FunctionAll>Hive Date FunctionTry Databricks for freeGet StartedWhat is a Hive Date Function?Hive provides many built-in functions to help us in the processing and querying of data. Some of the functionalities provided by these functions include string manipulation, date manipulation, type conversion, conditional operators, mathematical functions, and several others.Types of Built-in Functions in HIVEDate FunctionsMainly used to perform operations on date data types such as adding the number of days to the date or other similar operations.Mathematical FunctionsThese functions are primarily used to perform mathematical calculations.Conditional FunctionsThese functions are used to test conditions and returns a value based on whether the test condition is true or false.String FunctionsThese are used to perform operations on strings such as finding the length of a string etc.Collection FunctionsThese functions are used to find the size of the complex types like array and map. There is one collection function and that is SIZE. The SIZE function's main usage is to find the number of elements in an array and map.Type Conversion FunctionThis function's usage is to convert the data from one type to another. The only type conversion function is CAST.Table Generating FunctionsThese functions can be used to turn a single row into multiple rows. EXPLODE is the only table generated function. This function uses an array as an input and outputs the elements of the array into separate rows.Date TypesAre highly formatted; in their case, each date value contains the century, year, month, day, hour, minute, and second. These functions are used to perform operations on date data types like adding the number of days to the date, conversion of Date types from one type to another type etc. Below are the most commonly used Hadoop Hive DateTime functions:Function NameReturn TypeDescriptionUnix_TimestampBigIntIt will get current Unix timestamp in seconds.To_date(string timestamp)StringIt will fetch and give the date part of a timestamp string:year(string date)INTIt will fetch and give the year part of a date or a timestamp string.quarter (date/timestamp/string)INTThe function quarter was introduced in Hive 1.3, and it will fetch and give the quarter of the year for a date, timestamp, or string in the range 1 to 4month(string date)INTIt will give the month part of a date or a timestamp string.hour(string date)INTThe hour function will fetch and gives the hour of the timestampminute(string date)INTThis function will return minute from the timestampDate_sub(string starting date, int days)stringThe DATE_SUB function subtracts the number of days to the specified dateCurrent_datedateIt will return the current date at the start of query evaluation.LAST _day(string date)stringIt will fetch and give the last day of the month which the date belongs totrunc(string date, string format)stringThis function strips off fields from a TIMESTAMP valueAdditional ResourcesExperiences Migrating Hive Workload to SparkSQLFunctions DocumentationBack 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.
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https://www.databricks.com/br/resources/analyst-paper/databricks-named-leader-by-gartner?itm_data=gartnermqcdbms22 | Databricks escolhe líder | Databricks2022 Gartner® Magic Quadrant™Databricks escolhe líderSistemas de gerenciamento de banco de dados na nuvemDatabricks foi nomeada líder no 2022 Gartner® Magic Quadrant™ CDBMSA Databricks tem o orgulho de anunciar que a Gartner nos nomeou líder no 2022 Magic Quadrant para Sistemas de Gerenciamento de Banco de Dados na Nuvem pelo segundo ano consecutivo.Acreditamos que esse reconhecimento valida nossa visão para o lakehouse como uma plataforma única e unificada para gerenciamento e engenharia de dados – bem como análise e IA.Baixe o relatório para saber por que a Gartner nomeou a Databricks líder e para obter mais informações sobre os benefícios que uma plataforma de lakehouse pode trazer para sua organização.Acesse o relatório 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.ProdutoVisão 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.
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https://www.databricks.com/de/company/board-of-directors | Board of Directors von Databricks | 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 NOWFührungUnser Führungsteam verfolgt eine langfristige Vision und nutzt dabei unsere jahrzehntelange Erfahrung, um einen neuen Kurs für Daten und KI zu entwickeln.Unser TeamLeitungsteamGründerBoard of DirectorsIon StoicaMitbegründer und geschäftsführender VorsitzenderBen HorowitzMitbegründer von Andreessen HorowitzElena DonioMitglied im Board of DirectorsAli GhodsiMitbegründer und Chief Executive OfficerPete SonsiniGeneral Partner bei New Enterprise AssociatesJonathan ChadwickMitglied im Board of DirectorsMatei ZahariaMitbegründer und Chief TechnologistScott ShenkerProfessor für Informatik an der UC BerkeleyProduktPlatform 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.
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https://www.databricks.com/glossary/predictive-maintenance | Predictive MaintenancePlatformThe 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 NOWPredictive MaintenanceAll>Predictive MaintenanceTry Databricks for freeGet StartedWhat is predictive maintenance?Predictive Maintenance, in a nutshell, is all about figuring out when an asset should be maintained, and what specific maintenance activities need to be performed, based on an asset’s actual condition or state, rather than on a fixed schedule, so that you can maximize uptime and productivity. It is all about predicting & preventing failures and performing the right maintenance routines in order to reduce costly equipment downtimes.With IoT and sensor data streaming from equipment, predictive maintenance enables Manufacturers to effectively predict machine outages. The data detects variances, understands warning signals, and identifies any patterns that may indicate a potential breakdown. Manufacturers can use analytics and machine learning to accurately predict the odds of a machine going down. This enables early and corrective measures to be planned (i.e., spare parts ordering, repair scheduling, etc.) and introduced in the most effective way, thereby avoiding unplanned downtime and costly staff and resources.Why is predictive maintenance important?Using IoT and data analytics to predict and prevent breakdowns can reduce overall downtime by 50%. (McKinsey)What are Databricks’ differentiated capabilities?Databricks’ Lakehouse uses technologies that include Delta, Delta Live Tables, Autoloader and Photon to enable customers to make data available for real-time decisions.Lakehouse for MFG supports the largest data jobs at near real-time intervals. For example, customers are bringing nearly 400 million events per day from transactional log systems at 15-second intervals. Because of the disruption to reporting and analysis that occurs during data processing, most retail customers load data to their data warehouse during a nightly batch. Some companies are even loading data weekly or monthly.A Lakehouse event-driven architecture provides a simpler method of ingesting and processing batch and streaming data than legacy approaches, such as lambda architectures. This architecture handles the change data capture and provides ACID compliance to transactions.Delta Live Tables simplifies the creation of data pipelines and automatically builds in lineage to assist with ongoing management.The Lakehouse allows for real-time stream ingestion of data and analytics on streaming data. Data warehouses require the extraction, transformation, loading, and additional extraction from the data warehouse to perform any analytics.Photon provides record-setting query performance, enabling users to query even the largest of data sets to power real-time decisions in BI tools.Additional ResourcesPredictive Maintenance interactive eBookDatabricks solutions for ManufacturingBack 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.
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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/bin-mu/# | Bin Mu - 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 ExperiencePricingBin MuVice President and Head of Data & Analytics at AdobeBack 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/university | Databricks University Alliance for Aspiring Data Scientists | 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.
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https://www.databricks.com/blog/2022/08/09/low-latency-streaming-data-pipelines-with-delta-live-tables-and-apache-kafka.html | Declarative Streaming Data Pipelines with Delta Live Tables and Apache Kafka - 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
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Many use cases require actionable insights derived from near real-time data. Delta Live Tables enables low-latency streaming data pipelines to support such use cases with low latencies by directly ingesting data from event buses like Apache Kafka, AWS Kinesis, Confluent Cloud, Amazon MSK, or Azure Event Hubs.This article will walk through using DLT with Apache Kafka while providing the required Python code to ingest streams. The recommended system architecture will be explained, and related DLT settings worth considering will be explored along the way.Streaming platformsEvent buses or message buses decouple message producers from consumers. A popular streaming use case is the collection of click-through data from users navigating a website where every user interaction is stored as an event in Apache Kafka. The event stream from Kafka is then used for real-time streaming data analytics. Multiple message consumers can read the same data from Kafka and use the data to learn about audience interests, conversion rates, and bounce reasons. The real-time, streaming event data from the user interactions often also needs to be correlated with actual purchases stored in a billing database.Apache KafkaApache Kafka is a popular open source event bus. Kafka uses the concept of a topic, an append-only distributed log of events where messages are buffered for a certain amount of time. Although messages in Kafka are not deleted once they are consumed, they are also not stored indefinitely. The message retention for Kafka can be configured per topic and defaults to 7 days. Expired messages will be deleted eventually.This article is centered around Apache Kafka; however, the concepts discussed also apply to many other event busses or messaging systems.Streaming data pipelinesIn a data flow pipeline, Delta Live Tables and their dependencies can be declared with a standard SQL Create Table As Select (CTAS) statement and the DLT keyword "live."When developing DLT with Python, the @dlt.table decorator is used to create a Delta Live Table. To ensure the data quality in a pipeline, DLT uses Expectations which are simple SQL constraints clauses that define the pipeline's behavior with invalid records.Since streaming workloads often come with unpredictable data volumes, Databricks employs enhanced autoscaling for data flow pipelines to minimize the overall end-to-end latency while reducing cost by shutting down unnecessary infrastructure.Delta Live Tables are fully recomputed, in the right order, exactly once for each pipeline run.In contrast, streaming Delta Live Tables are stateful, incrementally computed and only process data that has been added since the last pipeline run. If the query which defines a streaming live tables changes, new data will be processed based on the new query but existing data is not recomputed. Streaming live tables always use a streaming source and only work over append-only streams, such as Kafka, Kinesis, or Auto Loader. Streaming DLTs are based on top of Spark Structured Streaming.You can chain multiple streaming pipelines, for example, workloads with very large data volume and low latency requirements.Direct Ingestion from Streaming EnginesDelta Live Tables written in Python can directly ingest data from an event bus like Kafka using Spark Structured Streaming. You can set a short retention period for the Kafka topic to avoid compliance issues, reduce costs and then benefit from the cheap, elastic and governable storage that Delta provides.As a first step in the pipeline, we recommend ingesting the data as is to a bronze (raw) table and avoid complex transformations that could drop important data. Like any Delta Table the bronze table will retain the history and allow to perform GDPR and other compliance tasks.Ingest streaming data from Apache KafkaWhen writing DLT pipelines in Python, you use the @dlt.table annotation to create a DLT table. There is no special attribute to mark streaming DLTs in Python; simply use spark.readStream() to access the stream. Example code for creating a DLT table with the name kafka_bronze that is consuming data from a Kafka topic looks as follows:
import dlt
from pyspark.sql.functions import *
from pyspark.sql.types import *
TOPIC = "tracker-events"
KAFKA_BROKER = spark.conf.get("KAFKA_SERVER")
# subscribe to TOPIC at KAFKA_BROKER
raw_kafka_events = (spark.readStream
.format("kafka")
.option("subscribe", TOPIC)
.option("kafka.bootstrap.servers", KAFKA_BROKER)
.option("startingOffsets", "earliest")
.load()
)
@dlt.table(table_properties={"pipelines.reset.allowed":"false"})
def kafka_bronze():
return raw_kafka_eventspipelines.reset.allowedNote that event buses typically expire messages after a certain period of time, whereas Delta is designed for infinite retention.This might lead to the effect that source data on Kafka has already been deleted when running a full refresh for a DLT pipeline. In this case, not all historic data could be backfilled from the messaging platform, and data would be missing in DLT tables. To prevent dropping data, use the following DLT table property:pipelines.reset.allowed=falseSetting pipelines.reset.allowed to false prevents refreshes to the table but does not prevent incremental writes to the tables or new data from flowing into the table.CheckpointingIf you are an experienced Spark Structured Streaming developer, you will notice the absence of checkpointing in the above code. In Spark Structured Streaming checkpointing is required to persist progress information about what data has been successfully processed and upon failure, this metadata is used to restart a failed query exactly where it left off.Whereas checkpoints are necessary for failure recovery with exactly-once guarantees in Spark Structured Streaming, DLT handles state automatically without any manual configuration or explicit checkpointing required.Mixing SQL and Python for a DLT PipelineA DLT pipeline can consist of multiple notebooks but one DLT notebook is required to be either entirely written in SQL or Python (unlike other Databricks notebooks where you can have cells of different languages in a single notebook).Now, if your preference is SQL, you can code the data ingestion from Apache Kafka in one notebook in Python and then implement the transformation logic of your data pipelines in another notebook in SQL.Schema mappingWhen reading data from messaging platform, the data stream is opaque and a schema has to be provided.The Python example below shows the schema definition of events from a fitness tracker, and how the value part of the Kafka message is mapped to that schema.
event_schema = StructType([ \
StructField("time", TimestampType(),True) , \
StructField("version", StringType(),True), \
StructField("model", StringType(),True) , \
StructField("heart_bpm", IntegerType(),True), \
StructField("kcal", IntegerType(),True) \
])
# temporary table, visible in pipeline but not in data browser,
# cannot be queried interactively
@dlt.table(comment="real schema for Kakfa payload",
temporary=True)
def kafka_silver():
return (
# kafka streams are (timestamp,value)
# value contains the kafka payload
dlt.read_stream("kafka_bronze")
.select(col("timestamp"),from_json(col("value")
.cast("string"), event_schema).alias("event"))
.select("timestamp", "event.*")
)
BenefitsReading streaming data in DLT directly from a message broker minimizes the architectural complexity and provides lower end-to-end latency since data is directly streamed from the messaging broker and no intermediary step is involved.Streaming Ingest with Cloud Object Store IntermediaryFor some specific use cases you may want offload data from Apache Kafka, e.g., using a Kafka connector, and store your streaming data in a cloud object intermediary. In a Databricks workspace, the cloud vendor-specific object-store can then be mapped via the Databricks Files System (DBFS) as a cloud-independent folder. Once the data is offloaded, Databricks Auto Loader can ingest the files.Auto Loader can ingest data with with a single line of SQL code. The syntax to ingest JSON files into a DLT table is shown below (it is wrapped across two lines for readability).
-- INGEST with Auto Loader
create or replace streaming live table raw
as select * FROM cloud_files("dbfs:/data/twitter", "json")
Note that Auto Loader itself is a streaming data source and all newly arrived files will be processed exactly once, hence the streaming keyword for the raw table that indicates data is ingested incrementally to that table.Since offloading streaming data to a cloud object store introduces an additional step in your system architecture it will also increase the end-to-end latency and create additional storage costs. Keep in mind that the Kafka connector writing event data to the cloud object store needs to be managed, increasing operational complexity.Therefore Databricks recommends as a best practice to directly access event bus data from DLT using Spark Structured Streaming as described above.Other Event Buses or Messaging SystemsThis article is centered around Apache Kafka; however, the concepts discussed also apply to other event buses or messaging systems. DLT supports any data source that Databricks Runtime directly supports.Amazon KinesisIn Kinesis, you write messages to a fully managed serverless stream. Same as Kafka, Kinesis does not permanently store messages. The default message retention in Kinesis is one day.When using Amazon Kinesis, replace format("kafka") with format("kinesis") in the Python code for streaming ingestion above and add Amazon Kinesis-specific settings with option(). For more information, check the section about Kinesis Integration in the Spark Structured Streaming documentation.Azure Event HubsFor Azure Event Hubs settings, check the official documentation at Microsoft and the article Delta Live Tables recipes: Consuming from Azure Event Hubs.SummaryDLT is much more than just the "T" in ETL. With DLT, you can easily ingest from streaming and batch sources, cleanse and transform data on the Databricks Lakehouse Platform on any cloud with guaranteed data quality.Data from Apache Kafka can be ingested by directly connecting to a Kafka broker from a DLT notebook in Python. Data loss can be prevented for a full pipeline refresh even when the source data in the Kafka streaming layer expired.Get startedIf you are a Databricks customer, simply follow the guide to get started. Read the release notes to learn more about what's included in this GA release. If you are not an existing Databricks customer, sign up for a free trial, and you can view our detailed DLT Pricing here.Join the conversation in the Databricks Community where data-obsessed peers are chatting about Data + AI Summit 2022 announcements and updates. Learn. Network.Last but not least, enjoy the Dive Deeper into Data Engineering session from the summit. In that session, I walk you through the code of another streaming data example with a Twitter live stream, Auto Loader, Delta Live Tables in SQL, and Hugging Face sentiment analysis.Try Databricks for freeGet StartedRelated postsAnnouncing General Availability of Databricks’ Delta Live Tables (DLT)April 5, 2022 by Michael Armbrust, Awez Syed, Paul Lappas, Erika Ehrli, Sam Steiny, Richard Tomlinson, Andreas Neumann and Mukul Murthy in Platform Blog
Today, we are thrilled to announce that Delta Live Tables (DLT) is generally available (GA) on the Amazon AWS and Microsoft Azure clouds...
Delta Live Tables Announces New Capabilities and Performance OptimizationsJune 29, 2022 by Paul Lappas and Michael Armbrust in Product
Since the availability of Delta Live Tables (DLT) on all clouds in April (announcement), we've introduced new features to make development easier, enhanced...
5 Steps to Implementing Intelligent Data Pipelines With Delta Live TablesSeptember 8, 2021 by Awez Syed and Amit Kara in Platform Blog
Get an early preview of O'Reilly's new ebook for the step-by-step guidance you need to start using Delta Lake Many IT organizations are...
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https://www.databricks.com/solutions/industries/financial-services | Lakehouse for Financial Services - 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.
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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 NOWLakehouse for Financial ServicesPowering the data and AI–driven financial services institution of the futureGet startedSchedule a demoFour data challenges in financial servicesData governance and management
Lack of data agility and model reproducibility makes it challenging to meet the regulatory requirements unique to financial services.Deeper customer insights
Data silos prevent a complete view of customer behaviors, cross-selling opportunities and the insights needed for hyper-personalization at scale.Real-time decisions
Vendor lock-in and disjointed tools hinder the ability to do real-time analytics that drive and democratize smarter financial decisions.Access to third-party data
Legacy technologies can’t harness financial and customer insights from fast-growing unstructured and alternative data sets and don’t offer open data sharing capabilities to fuel collaboration.Lakehouse for Financial ServicesUnified data and AI platform
A single platform that brings together all your data and analytics workloads to enable transformative innovations for modern financial services institutions.
Partner solutions
The world’s leading solution providers are building for the Lakehouse for Financial Services. Take advantage of pre-built offerings that accelerate data-driven transformation.
Tools to accelerate business outcomes
Databricks and its partners have created a full range of Solution Accelerators that make it easy to tackle common financial services use cases, from ESG investing to fraud prevention.
Industry collaboration
Enable secure and open data sharing with our data ecosystem — featuring S&P Global, Intercontinental Exchange, FactSet and Nasdaq — to unlock innovations that drive sustainable value creation.
Transforming financial services with
Lakehouse
“Nasdaq’s data and AI vision is powered by Databricks Lakehouse. We use it to process huge amounts of complex financial and alternative data to create data and insights for our clients. Databricks is also an important part of our efforts to modernize data delivery and consumption. It enables us to seamlessly deliver data directly into analytical workspaces, so our clients can analyze and integrate mission-critical data quickly without having to move terabytes of data around.”
— Bill Dague, Head of Nasdaq Data Link, NasdaqWhy Lakehouse for Financial Services?
Unify data and AI on an open and collaborative platform that empowers you to minimize risk, deliver superior customer experiences and accelerate innovationGoverned approach to risk management and compliance
Simplify the complexity of regulatory reporting, risk management and compliance by securely streamlining the acquisition, processing and transmission of data to empower better data governance practices.Personalized products and services
Unify a variety of data — from market to alternative data — enabling hyper-personalized experiences that drive cross-selling opportunities, customer satisfaction and share of wallet.Real-time insights, smarter decisions
Rapidly ingest all your data sources at scale to make better investment decisions, quickly detect new fraud patterns and bring real-time capabilities to risk management practices.Open data sharing and data monetization
Bring together vast amounts of internal and third-party data to share innovative financial solutions, monetize new data products and deliver advanced analytics capabilities to any cloud or tool without getting locked into proprietary technologies.Download the eBookPartners and solutionsMove toward open formats and the standardization of data for analytics and AIRisk ManagementRapidly deploy data into value-at-risk models to keep up with emerging risks and threats.Get startedLegacy Cards and Core Banking Portfolios ModernizationEnable rapid conversion from external source systems and achieve a fully configurable and industrialized conversion capability.Get startedPersona 360Complete, unify and fully capture customer profiles in a smart data model.Get startedSee all partner solutionsCybersecurity at scaleRapidly detect threats, investigate the impact and reduce risks with Splunk and DatabricksGet startedESG scoringTake a quantitative view into sustainability and ensure companies are accountable for their actionsGet startedModern risk managementAdopt a more agile approach to risk management by unifying data and AI in the LakehouseGet startedIdentify fraud with geospatial analytics and AIUse geospatial data to better understand customer spending behaviors in terms of both who they are and how they bankGet startedTransaction enrichment with merchant classificationAutomate transaction enrichment to better understand your customers’ behaviors and drive hyper-personalizationGet startedRule-based AI models to combat financial fraudModernize fraud-prevention strategies to reduce operational costs and increase customer trustGet startedTimely and reliable transmission of regulatory reportsCombine financial services industry data models with the cloud to enable high governance standards with low development overheadGet startedModernizing investment data platformsUse the full power of financial market data to focus on product delivery for customersGet startedAnti-money laundering (AML)Enable AI-driven use cases like fuzzy match and image analytics to combat money laundering and financial terrorismGet startedSee all solutionsLakehouse for Financial Services in actionSimplify secured deployment of Lakehouse with integrated industry best practices, unified governance and design patternsExplore nowResources
All the resources you need. All in one place.
Explore the resource library to find eBooks and videos on data and AI for financial services.
Explore resourceseBooks and Infographics[Infographic] Data to Anchor a New Age of Risk ManagementLearn how to easily tap into the power of data and AI in financial servicesLeveraging alternative and third-party data in financial servicesTaking ESG from buzzword to reality with data analytics and AI Preventing fraud with Data + AI: A primer for modern threats Videos and downloadsExplainable and Transparent ESG Investment MethodologiesCybersecurity in Financial ServicesAccelerate Data and AI-Driven Innovation in Financial ServicesESG analytics: explainer videoModernizing risk managementModern Data Investment Platforms with Lakehouse for Financial ServicesIndustry Lakehouse Blueprints Solution SheetBlogsAccelerator for banks and fintechs using credit card transactionsA data-driven approach to environmental, social and governanceBuilding a modern risk management platform in financial servicesUsing your data to stop credit card fraud: Capital One and other best practicesStrategies for modernizing investment data platformsImproving the customer experience with transaction enrichment Key Takeaways for Financial Services at Data + AI Summit 2022Lakehouse for Financial ServicesReady to get started?We’d love to understand your business goals and how our services team can help you succeed.Try Databricks for freeSchedule a demoProductPlatform 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.
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https://www.databricks.com/br/blog | O Blog 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
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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 NOWLoading...ProdutoVisão 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
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https://www.databricks.com/glossary | Glossaries Archive | 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.
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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 NOWGlossaryA-ZsearchACID Transactions What is a transaction?
In the context of databases and data storage systems, a transaction is any operation that is treated as a single unit of work, which either completes fully or does not complete at all, and leaves the storage system in a cons{...}AdaGrad Gradient descent is the most commonly used optimization method deployed in machine learning and deep learning algorithms. It’s used to train a machine learning model.
Types of Gradient Descent
There are three primary types of gradient descent {...}Alternative Data What is Alternative Data?
Alternative data is information gathered by using alternative sources of data that others are not using; non-traditional information sources. Analysis of alternative data can provide insights beyond that which an in{...}Anomaly Detection Anomaly Detection is the technique of identifying rare events or observations which can raise suspicions by being statistically different from the rest of the observations. Such “anomalous” behavior typically translates to some kind of a problem like{...}Apache Hive What is Apache Hive?
Apache Hive is open-source data warehouse software designed to read, write, and manage large datasets extracted from the Apache Hadoop Distributed File System (HDFS) , one aspect of a larger Hadoop Ecosystem.
With exten{...}Apache Kudu What is Apache Kudu?
Apache Kudu is a free and open source columnar storage system developed for the Apache Hadoop. It is an engine intended for structured data that supports low-latency random access millisecond-scale access to individual rows to{...}Apache Kylin What is Apache Kylin?
Apache Kylin is a distributed open source online analytics processing (OLAP) engine for interactive analytics Big Data. Apache Kylin has been designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/S{...}Apache Spark What Is Apache Spark?
Apache Spark is an open source analytics engine used for big data workloads. It can handle both batches as well as real-time analytics and data processing workloads. Apache Spark started in 2009 as a research project at {...}Apache Spark as a Service What is Apache Spark as a Service?
Apache Spark is an open source cluster computing framework for fast real-time large-scale data processing. Since its inception in 2009 at UC Berkeley’s AMPLab, Spark has seen major growth. It is currently ra{...}Artificial Neural Network What 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{...}Automation Bias What is Automation Bias?
Automation bias is an over-reliance on automated aids and decision support systems. As the availability of automated decision aids is increasing additions to critical decision-making contexts such as intensive care units, {...}Bayesian Neural Network What Are Bayesian Neural Networks?
Bayesian Neural Networks (BNNs) refers to extending standard networks with posterior inference in order to control over-fitting. From a broader perspective, the Bayesian approach uses the statistical methodology {...}Big Data Analytics The Difference Between Data and Big Data Analytics
Prior to the invention of Hadoop, the technologies underpinning modern storage and compute systems were relatively basic, limiting companies mostly to the analysis of "small data." Even this relat{...}Bioinformatics Bioinformatics is a field of study that uses computation to extract knowledge from large collections of biological data.
Bioinformatics refers to the use of IT in biotechnology for storing, retrieving, organizing and analyzing biological data.{...}Catalyst Optimizer At the core of Spark SQL is the Catalyst optimizer, which leverages advanced programming language features (e.g. Scala’s pattern matching and quasi quotes) in a novel way to build an extensible query optimizer. Catalyst is based on functional program{...}Complex Event Processing What is Complex Event Processing [CEP]?
Complex event processing [CEP] also known as event, stream or event stream processing is the use of technology for querying data before storing it within a database or, in some cases, without it ever being s{...}Continuous Applications Continuous applications are an end-to-end application that reacts to data in real-time. In particular, developers would like to use a single programming interface to support the facets of continuous applications that are currently handled in separate{...}Convolutional Layer In deep learning, a convolutional neural network (CNN or ConvNet) is a class of deep neural networks, that are typically used to recognize patterns present in images but they are also used for spatial data analysis, computer vision, natural language {...}Data Analysis Platform What is a Data Analysis Platform?
A data analytics platform is an ecosystem of services and technologies that needs to perform analysis on voluminous, complex and dynamic data that allows you to retrieve, combine, interact with, explore, and visua{...}Data Governance What is Data Governance?
Data governance is the oversight to ensure data brings value and supports the business strategy. Data governance is more than just a tool or a process. It aligns data-related requirements to the business strategy using a f{...}Data Lakehouse What is a Data Lakehouse?
A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business int{...}Data Mart What is a data mart?
A data mart is a curated database including a set of tables that are designed to serve the specific needs of a single data team, community, or line of business, like the marketing or engineering department. It is normally smal{...}Data Sharing What is data sharing?
Data sharing is the ability to make the same data available to one or many consumers. Nowadays, the ever-growing amount of data has become a strategic asset for any company. Sharing data - within your organization or external{...}Data Vault What is a data vault?
A data vault is a data modeling design pattern used to build a data warehouse for enterprise-scale analytics. The data vault has three types of entities: hubs, links, and satellites.
Hubs represent core business concepts, {...}Data Warehouse What is a data warehouse?
A data warehouse is a data management system that stores current and historical data from multiple sources in a business friendly manner for easier insights and reporting. Data warehouses are typically used for business i{...}Databricks Runtime Databricks Runtime is the set of software artifacts that run on the clusters of machines managed by Databricks. It includes Spark but also adds a number of components and updates that substantially improve the usability, performance, and security of {...}DataFrames What is a DataFrame?
A DataFrame is a data structure that organizes data into a 2-dimensional table of rows and columns, much like a spreadsheet. DataFrames are one of the most common data structures used in modern data analytics because they are {...}Datasets Datasets are a type-safe version of Spark's structured API for Java and Scala. This API is not available in Python and R, because those are dynamically typed languages, but it is a powerful tool for writing large applications in Scala and Java. Recal{...}Deep Learning What is Deep Learning?
Deep Learning is a subset of machine learning concerned with large amounts of data with algorithms that have been inspired by the structure and function of the human brain, which is why deep learning models are often referre{...}Demand Forecasting What is demand forecasting?
Demand forecasting is the process of projecting consumer demand (equating to future revenue). Specifically, it is projecting the assortment of products shoppers will buy using quantitative and qualitative data.
Ret{...}Dense Tensor Dense tensors store values in a contiguous sequential block of memory where all values are represented. Tensors or multi-dimensional arrays are used in a diverse set of multi-dimensional data analysis applications. There are a number of software prod{...}Digital Twin What is a Digital Twin?
The classical definition of of digital twin is; ""A digital twin is a virtual model designed to accurately reflect a physical object."" – IBM[KVK4] For a discrete or continuous manufacturing process, a digital twin gathers {...}DNA Sequence What is a DNA Sequence?
The DNA sequence is the process of determining the exact sequence of nucleotides of DNA (deoxyribonucleic acid). Sequencing DNA the order of the four chemical building blocks - adenine, guanine, cytosine, and thymine {...}Extract Transform Load (ETL) What is ETL?
As the amount of data, data sources, and data types at organizations grow, the importance of making use of that data in analytics, data science and machine learning initiatives to derive business insights grows as well. The need to pr{...}Feature Engineering Feature engineering for machine learning
Feature engineering, also called data preprocessing, is the process of converting raw data into features that can be used to develop machine learning models. This topic describes the principal concepts of f{...}Genomics Genomics is an area within genetics that concerns the sequencing and analysis of an organism's genome. Its main task is to determine the entire sequence of DNA or the composition of the atoms that make up the DNA and the chemical bonds between the DN{...}Hadoop Cluster What Is a Hadoop Cluster?
Apache Hadoop is an open source, Java-based, software framework and parallel data processing engine. It enables big data analytics processing tasks to be broken down into smaller tasks that can be performed in parallel by{...}Hadoop Distributed File System (HDFS) HDFS
HDFS (Hadoop Distributed File System) is the primary storage system used by Hadoop applications. This open source framework works by rapidly transferring data between nodes. It's often used by companies who need to handle and store big data. {...}Hadoop Ecosystem What is the Hadoop Ecosystem?
Apache Hadoop ecosystem refers to the various components of the Apache Hadoop software library; it includes open source projects as well as a complete range of complementary tools. Some of the most well-known tools of{...}Hash Buckets In computing, a hash table [hash map] is a data structure that provides virtually direct access to objects based on a key [a unique String or Integer]. A hash table uses a hash function to compute an index into an array of buckets or slots, from whic{...}Hive Date Function What is a Hive Date Function?
Hive provides many built-in functions to help us in the processing and querying of data. Some of the functionalities provided by these functions include string manipulation, date manipulation, type conversion, conditi{...}Hosted Spark What is Hosted Spark?
Apache Spark is a fast and general cluster computing system for Big Data built around speed, ease of use, and advanced analytics that was originally built in 2009 at UC Berkeley. It provides high-level APIs in Scala, Java, Py{...}Jupyter Notebook What is a Jupyter Notebook?
A Jupyter Notebook is an open source web application that allows data scientists to create and share documents that include live code, equations, and other multimedia resources.
What are Jupyter Notebooks used fo{...}Keras Model What is a Keras Model?
Keras is a high-level library for deep learning, built on top of Theano and Tensorflow. It is written in Python and provides a clean and convenient way to create a range of deep learning models. Keras has become one of {...}Lakehouse for Retail What is Lakehouse for Retail?
Lakehouse for Retail is Databricks’ first industry-specific Lakehouse. It helps retailers get up and running quickly through solution accelerators, data sharing capabilities, and a partner ecosystem.
Lakehouse fo{...}Lambda Architecture What is Lambda Architecture?
Lambda architecture is a way of processing massive quantities of data (i.e. "Big Data") that provides access to batch-processing and stream-processing methods with a hybrid approach. Lambda architecture is used to solv{...}Machine Learning Library (MLlib) Apache Spark’s Machine Learning Library (MLlib) is designed for simplicity, scalability, and easy integration with other tools. With the scalability, language compatibility, and speed of Spark, data scientists can focus on their data problems and mod{...}Machine Learning Models What is a machine learning Model?
A machine learning model is a program that can find patterns or make decisions from a previously unseen dataset. For example, in natural language processing, machine learning models can parse and correctly recogni{...}Managed Spark What is Managed Spark?
A managed Spark service lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. By using such an automation you will be able to quickly create clusters on -demand, m{...}MapReduce What is MapReduce?
MapReduce is a Java-based, distributed execution framework within the Apache Hadoop Ecosystem. It takes away the complexity of distributed programming by exposing two processing steps that developers implement: 1) Map and {...}Materialized views Delta Pipelines / Materialized Views in Databricks Delta
Intro
Delta Pipelines provides a set of APIs and UI for managing the data pipeline lifecycle. This open-source framework helps data engineering teams simplify ETL development, improve dat{...}Medallion Architecture What is a medallion architecture?
A medallion architecture is a data design pattern used to logically organize data in a lakehouse, with the goal of incrementally and progressively improving the structure and quality of data as it flows through ea{...}ML Pipelines Typically 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 c{...}MLOps What is MLOps?
MLOps stands for Machine Learning Operations. MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them. M{...}Model Risk Management Model risk management refers to the supervision of risks from the potential adverse consequences of decisions based on incorrect or misused models. The aim of model risk management is to employ techniques and practices that will identify, measure and{...}Neural Network What is a Neural Network?
A neural network is a computing model whose layered structure resembles the networked structure of neurons in the brain. It features interconnected processing elements called neurons that work together to produce an outpu{...}Open Banking What is Open Banking?
Open banking is a secure way to provide access to consumers' financial data, all contingent on customer consent.² Driven by regulatory, technology, and competitive dynamics, Open Banking calls for the democratization of custo{...}Orchestration What is Orchestration?
Orchestration is the coordination and management of multiple computer systems, applications and/or services, stringing together multiple tasks in order to execute a larger workflow or process. These processes can consist of {...}Overall Equipment Effectiveness What is Overall Equipment Effectiveness?
Overall Equipment Effectiveness(OEE) is a measure of how well a manufacturing operation is utilized (facilities, time and material) compared to its full potential, during the periods when it is scheduled to{...}pandas DataFrame
When it comes to data science, it's no exaggeration to say that you can transform the way your business works by using it to its full potential with pandas DataFrame. To do that, you'll need the right data structures. These will help you be as ef{...}Parquet What is Parquet?
Apache Parquet is an open source, column-oriented data file format designed for efficient data storage and retrieval. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in {...}Personalized Finance What is Personalized Finance?
Financial products and services are becoming increasingly commoditized and consumers are becoming more discerning as the media and retail industries have increased their penchant for personalized experiences. To remai{...}Predictive Analytics What is Predictive Analytics?
Predictive analytics is a form of advanced analytics that uses both new and historical data to determine patterns and predict future outcomes and trends.
How Does Predictive Analytics Work?
Predictive analytics {...}Predictive Maintenance What is predictive maintenance?
Predictive Maintenance, in a nutshell, is all about figuring out when an asset should be maintained, and what specific maintenance activities need to be performed, based on an asset’s actual condition or state, rath{...}PyCharm PyCharm is an integrated development environment (IDE) used in computer programming, created for the Python programming language. When using PyCharm on Databricks, by default PyCharm creates a Python Virtual Environment, but you can configure to crea{...}PySpark What is PySpark?
Apache Spark is written in Scala programming language. PySpark has been released in order to support the collaboration of Apache Spark and Python, it actually is a Python API for Spark. In addition, PySpark, helps you interface wi{...}Real-Time Retail What is real-time data for Retail?
Real-time retail is real-time access to data. Moving from batch-oriented access, analysis and compute will allow data to be “always on,” therefore driving accurate, timely decisions and business intelligence. {...}Resilient Distributed Dataset (RDD)
RDD was the primary user-facing API in Spark since its inception. At the core, an RDD is an immutable distributed collection of elements of your data, partitioned across nodes in your cluster that can be operated in parallel with a low-level API {...}Snowflake Schema What is a snowflake schema?
A snowflake schema is a multi-dimensional data model that is an extension of a star schema, where dimension tables are broken down into subdimensions. Snowflake schemas are commonly used for business intelligence and re{...}Spark API If you are working with Spark, you will come across the three APIs: DataFrames, Datasets, and RDDs
What are Resilient Distributed Datasets?
RDD or Resilient Distributed Datasets, is a collection of records with distributed computing, which are {...}Spark Applications Spark Applications consist of a driver process and a set of executor processes. The driver process runs your main() function, sits on a node in the cluster, and is responsible for three things: maintaining information about the Spark Application; res{...}Spark Elasticsearch What 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 r{...}Spark SQL Many data scientists, analysts, and general business intelligence users rely on interactive SQL queries for exploring data. Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can al{...}Spark Streaming Apache Spark Streaming is the previous generation of Apache Spark’s streaming engine. There are no longer updates to Spark Streaming and it’s a legacy project. There is a newer and easier to use streaming engine in Apache Spark called Structured Stre{...}Spark Tuning What is Spark Performance Tuning?
Spark Performance Tuning refers to the process of adjusting settings to record for memory, cores, and instances used by the system. This process guarantees that the Spark has a flawless performance and also preven{...}Sparklyr What 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 l{...}SparkR SparkR is a tool for running R on Spark. It follows the same principles as all of Spark’s other language bindings. To use SparkR, we simply import it into our environment and run our code. It’s all very similar to the Python API except that it follow{...}Sparse Tensor Python offers an inbuilt library called numpy to manipulate multi-dimensional arrays. The organization and use of this library is a primary requirement for developing the pytensor library. Sptensor is a class that represents the sparse tensor. A spa{...}Star Schema What is a star schema?
A star schema is a multi-dimensional data model used to organize data in a database so that it is easy to understand and analyze. Star schemas can be applied to data warehouses, databases, data marts, and other tools. The st{...}Streaming Analytics How Does Stream Analytics Work?
Streaming analytics, also known as event stream processing, is the analysis of huge pools of current and “in-motion” data through the use of continuous queries, called event streams. These streams are triggered by a{...}Structured Streaming Structured Streaming is a high-level API for stream processing that became production-ready in Spark 2.2. Structured Streaming allows you to take the same operations that you perform in batch mode using Spark’s structured APIs, and run them in a stre{...}Supply Chain Management What is supply chain management?
Supply chain management is the process of planning, implementing and controlling operations of the supply chain with the goal of efficiently and effectively producing and delivering products and services to the end{...}TensorFlow In November of 2015, Google released its open-source framework for machine learning and named it TensorFlow. It supports deep-learning, neural networks, and general numerical computations on CPUs, GPUs, and clusters of GPUs. One of the biggest advant{...}Tensorflow Estimator API What is the Tensorflow Estimator API?
Estimators represent a complete model but also look intuitive enough to less user. The Estimator API provides methods to train the model, to judge the model’s accuracy, and to generate predictions. TensorFlow {...}Transformations What 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” {...}Tungsten What is the Tungsten Project?
Tungsten is the codename for the umbrella project to make changes to Apache Spark’s execution engine that focuses on substantially improving the efficiency of memory and CPU for Spark applications, to push performance{...}Unified AI Framework Unified Artificial Intelligence or UAI was announced by Facebook during F8 this year. This brings together 2 specific deep learning frameworks that Facebook created and outsourced - PyTorch focused on research assuming access to large-scale compute r{...}Unified Data Analytics Unified Data Analytics is a new category of solutions that unify data processing with AI technologies, making AI much more achievable for enterprise organizations and enabling them to accelerate their AI initiatives. Unified Data Analytics makes it e{...}Unified Data Analytics Platform Databricks' Unified Data Analytics Platform helps organizations accelerate innovation by unifying data science with engineering and business. With Databricks as your Unified Data Analytics Platform, you can quickly prepare and clean data at mass{...}Unified Data Warehouse What is a Unified Data Warehouse?
A unified database also known as an enterprise data warehouse holds all the business information of an organization and makes it accessible all across the company. Most companies today, have their data managed in {...}What Is Hadoop? Apache Hadoop is an open source, Java-based software platform that manages data processing and storage for big data applications. The platform works by distributing Hadoop big data and analytics jobs across nodes in a computing cluster, breaking them{...}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
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https://www.databricks.com/product/data-science | Data Science | 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
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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 NOWData ScienceCollaborative data science at scaleTry for freeSchedule a demoDive deeper into Data Science on DatabricksDatabricks NotebooksIDE IntegrationsDatabricks WorkflowsReposMachine LearningStreamline the end-to-end data science workflow — from data prep to modeling to sharing insights — with a collaborative and unified data science environment built on an open lakehouse foundation. Get quick access to clean and reliable data, preconfigured compute resources, IDE integration, multi-language support, and built-in advanced visualization tools for maximum flexibility for data analytics teams.Collaboration across the entire data science workflowWrite code in Python, R, Scala and SQL, explore data with interactive visualizations and discover new insights with Databricks Notebooks. Confidently and securely share code with coauthoring, commenting, automatic versioning, Git integrations, and role-based access controls.Focus on the data science, not the infrastructureYou don’t have to be limited by how much data fits on your laptop anymore or how much compute is available to you. Quickly migrate your local environment to the cloud and connect notebooks to your own personal compute and auto-managed clusters.Use your favorite local IDE with scalable computeThe choice of an IDE is very personal and affects productivity significantly. Connect your favorite IDE to Databricks, so that you can still benefit from limitless data storage and compute. Or simply use RStudio or JupyterLab directly from within Databricks for a seamless experience.Get data ready for data scienceClean and catalog all your data — batch, streaming, structured or unstructured — in one place with Delta Lake and make it discoverable to your entire organization via a centralized data store. As data comes in, automatic quality checks ensure data meets expectations and is ready for analytics. As data evolves with new data and further transformations, data versioning ensures you can meet compliance needs.Low-code, visual tools for data explorationUse visual tools natively from within Databricks notebooks to prepare, transform and analyze your data, enabling teams across expertise levels to work with data. Once done with your data transformations and visualizations, you can generate the code that’s running in the background — saving you time from writing boilerplate code so you can spend more time on high-value work.Discover and share new insightsEasily share and export results by quickly turning your analysis into a dynamic dashboard. The dashboards are always up to date and can also run interactive queries. Cells, visualizations or notebooks can be shared with role-based access control and exported in multiple formats, including HTML and IPython Notebook.Migrate to DatabricksTired of the data silos, slow performance and high costs associated with legacy systems like Hadoop and enterprise data warehouses? Migrate to the Databricks Lakehouse: the modern platform for all your data, analytics and AI use cases.Migrate to DatabricksResources
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Explore resourcesReports and eBooksThe Big Book of Data ScienceThe Art of Collaborative Data Science at ScaleModern Cloud Data PlatformMLflow – An open source machine learning platformExplore the new Delta Sharing solutionDatabricks named a Leader in 2021 Gartner® Magic Quadrant™ in both DBMS and DSMLMigration Guide: Hadoop to DatabricksBlogs5 Key Steps to Successfully Migrate From Hadoop to the Lakehouse ArchitectureVirtual EventsStep-by-step Guide to Hadoop MigrationReady 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/dataaisummit/speaker/michael-carbin/# | Michael Carbin - 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 CarbinFounding Advisor, MosaicML and Associate Professor, Department of Electrical Engineering and Computer Science at Massachusetts Institute of TechnologyBack to speakersMichael Carbin is an associate professor in MIT’s Department of Electrical Engineering and Computer Science and a principal investigator at the Computer Science and Artificial Intelligence Laboratory, where he leads the Programming Systems Group. His group investigates the semantics, design, and implementation of systems that operate in the presence of uncertainty in their environment (perception), implementation (neural networks or approximate transformations), or execution (unreliable hardware). Carbin has received a Sloan Research Fellowship, a Facebook Research Award, a Google Faculty Research Award and an NSF Career Award. He earned a BS in computer science at Stanford University and an MS and PhD in electrical engineering and computer science from MITLooking 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/ifigeneia-derekli/# | Ifigeneia Derekli - 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 ExperiencePricingIfigeneia DerekliField Engineering Manager & Unity Catalog Specialist at DatabricksBack to speakersField Engineering Manager at 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/dataaisummit/speaker/vuong-nguyen/# | Vuong Nguyen - 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 ExperiencePricingVuong NguyenSenior Solutions Architect at DatabricksBack to speakersA Senior Solutions Architect at Databricks based in London, with a strong passion to help customers with their data governance, data sharing and platform automation needs. Regular contributor to open source projects such as the Databricks Terraform Provider and Delta Sharing.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/it/resources | Resources - DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
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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 NOWResourcesLoading...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
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https://www.databricks.com/dataaisummit/speaker/matei-zaharia/# | Matei Zaharia - 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 ExperiencePricingMatei ZahariaCo-founder and Chief Technologist; Original Creator of Apache Spark™ and MLflow at DatabricksBack to speakersMatei Zaharia is a Cofounder and Chief Technologist at Databricks as well as an Assistant Professor of Computer Science at Stanford. He started the Apache Spark project during his PhD at UC Berkeley in 2009, and has worked broadly on other widely used data and machine learning software, including MLflow, Delta Lake and Apache Mesos. He works on a wide variety of projects in data management and machine learning at Databricks and Stanford. Matei’s research was recognized through the 2014 ACM Doctoral Dissertation Award, an NSF CAREER Award, and the US Presidential Early Career Award for Scientists and Engineers (PECASE).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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