Navigating Microsoft Fabric - Choosing the Right Workload for Your Needs
summary
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.
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chapters (11)
- 0:00Introduction
- 1:55Choosing analytical engine
- 3:20Lakehouse use cases
- 6:52Shortcuts and ingestion
- 8:19Demo: Lakehouse creation
- 18:12Fabric Data Warehouse
- 23:54Lakehouse vs Warehouse
- 35:24Real-time analytics with KQL
- 45:16Power BI storage modes
- 47:06Power BI models with OneLake
- 50:41Q&A
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