使用 Power BI 和 Databricks 进行流式处理数据分析.pdf

1、Streaming data analytics with Power BI and DatabricksLiping HuangMarius PangaDatabricks2023Agenda Lakehouse for Real-Time Analytics Architectural Patterns and Demos Considerations2Transactional recordsPoint of sale(POS)Banking transactionsAirline reservationsCall center recordsInteractionsWeb clicks

2、Social postsEmailsInstant messagesIoT eventsSensorsGeolocationMachine logsMobile devicesThird-partyNews feedsWeatherMarket dataReal-time trafficFrauddetectionPersonalized offerVaccine distributionPredictivemaintenanceSmart pricingIn-game analyticsConnected carsand smart devicesContentrecommendations

3、New opportunities from real-time dataEvery organization generates vast amounts of real-time dataCreating opportunities for new kinds of real-time applicationsReal-time and historical data in separate systemsData streaming is hard for most organizationsDifficult to enable existing data teams with the

4、 languages and tools they already knowDifficult to deploy and maintain streaming data pipelines that run reliably in your production environmentNew APIs and languages to learnComplex operational tooling to buildIncompatible governance models limit ability to control access for the right users and gr

5、oupsLakehouse PlatformDataWarehousingData EngineeringData Scienceand MLData StreamingAll structured and unstructured dataCloud Data LakeUnity CatalogUnified governance for all data assetsDelta LakeData reliability and performanceEnable all your data teamsData engineers,data scientists,and analysts c

6、an easily build streaming data pipelines with the languages and tools they already know.Simplify development and operationsReduce complexity by automating many of the production aspects associated with building and maintaining real-time data workflows.One platform for streaming batch and dataElimina