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Navigating Microsoft Fabric - Choosing the Right Workload for Your Needs

Talkat SQLBits 2024 · 2024-03-19..03-23· [databases]
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Microsoft Fabric walks through three concrete workloads and tradeoffs: a Spark-powered Lakehouse that you can create automatically and do a no-code Lakehouse load from uploaded files or shortcuts to external storage, but which requires Delta/Parquet optimization and vacuuming for updates; a Warehouse that exposes a SQL analytical endpoint supporting inserts/updates and automated file optimization (Polaris engine) but currently lacks MERGE/identity support; and a KQL real‑time pipeline demo that ingests Event Hub telemetry and drives a live Power BI report successfully.

AI-written summary · about

  1. 0:00Introduction
  2. 1:55Choosing analytical engine
  3. 3:20Lakehouse use cases
  4. 6:52Shortcuts and ingestion
  5. 8:19Demo: Lakehouse creation
  6. 18:12Fabric Data Warehouse
  7. 23:54Lakehouse vs Warehouse
  8. 35:24Real-time analytics with KQL
  9. 45:16Power BI storage modes
  10. 47:06Power BI models with OneLake
  11. 50:41Q&A
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