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Bi and AI updates in the Microsoft Data Platform stack

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Discovery Day 2019 Bi and AI updates in the Microsoft Data Platform stack

Veröffentlicht in: Technologie
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Bi and AI updates in the Microsoft Data Platform stack

  1. 1. Powered by BI and AI in the Microsoft data platform universe (with a dash of cloud) Ivan Donev
  2. 2. Agenda • What’s new in SQL 2019 for BI • Important updates on Azure for Data platform • AI and ML for the masses
  3. 3. What is new in 2019 for BI • SQL Server AS • MD nothing • Tabular – Calc groups, M:M relationships, dynamic formatting • SQL Server IS • Nothing • SQL Server RS • Nothing • SQL Server MDM and DQS • Almost nothing
  4. 4. DEMO with AS 2019 Calculation groups Dynamic formatting M:M in tabular
  5. 5. Important Azure Milestones
  6. 6. Updates in Azure SQL DB • Azure SQL DB – Hyperscale• Azure SQL DB – Serverless
  7. 7. Azure SQL DB – Serverless • Single DB Serverless compute tier • Billed on compute used per SECOND • Used only in the vCore model • Parametrize the min/max vCores • Scenarios • Intermittent usage • Frequently rescaled DBs • New deployments prior historical usage data
  8. 8. Azure SQL DB – Hyperscale • Up to 100TB • Fast backups (filesystem snapshots) • Up to a minute restores • Faster throughput • Fast scale out and scale up • Distributed architecture
  9. 9. Updates in the DWH and Big Data world
  10. 10. The modern datawarehouse layout
  11. 11. Modern DWH – important updates in Ingest • Azure Data Factory v2 • Integration runtimes to run SSIS as-is • Storing SSIS catalogue in SQL DB • Mapping workflow • Wrangling workflow
  12. 12. Data factory v2 – Mapping workflow
  13. 13. Data factory v2 – wrangling dataflow
  14. 14. Modern DWH – important updates in Store • Azure Data Lake Gen 2 • Hierarchical file system • Security • Performance • Much easier to integrate with other services
  15. 15. Modern DWH – important updates in Prep and Train • Azure Databricks Delta • Spark engine with RDBMS features
  16. 16. Databricks Delta • ACID transactions • Versioned PARQUET files • Streaming writes to a table (i.e. Kafka) • Batch upserts • High performance reads • Schema enforcement
  17. 17. Modern DWH – important updates in Model and Serve • Changes in Azure DWH • Concurrency increased to 128 • Adaptive caching (NVMe !!!) • Unlimited Columnstore storage capacity • Workload classification and importance improvements • Changes in PowerBI
  18. 18. PowerBI Updates worth noting • PowerBI Dataflows • Self-service data transformation • Shared and certified datasets (preview) • Paginated Reports (SSRS) • Premium • XMLA Endpoints • Premium • Auto ML • Premium • CDS integration
  19. 19. The AI in BI • Options to use in DWH and BI
  20. 20. The AI in BI • Options to use in DWH and BI
  21. 21. The ML in BI Not scalableSelf-service AI •Prototyping •Do not need additional configuration or tuning •Options are •Microsoft Cognitive services with PowerBI (demo) •AutoML in Dataflows in PowerBI Premium •R/Python visuals Scalable, configurable, needs specialized staffEnterprise AI •Mandatory to run ML and store it in the Store/Serve model •Options are •Databricks •Azure ML •R/Python in Dataflow as data sources
  22. 22. How to choose? • The aim? • Prototype/Test/Verify • Production/O16N • The knowledge • R/Python/Scala/Java/… • The task • Image processing/Text analytics/Prediction/Classification • The post-production support • Can you support the solution afterwards?
  23. 23. Demo • PowerBI Dataflows • Using External Cognitive Services APIs
  24. 24. THANK YOU All my demos will be described and uploaded on our blog: http://sqlmasteracademy.com/techblog/

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