What's changed: Added §4.4 "Microsoft Fabric (Unified Analytics Platform)" to fix the drift from the 2026 refresh that centers the analytics domain on Fabric/OneLake/lakehouse/Real-Time Intelligence/Data Activator. Adds Fabric alongside the existing Synapse-centric content
4.1The Analytics Pipeline, Data Warehouse, and Data Lake
Understand the typical analytics pipeline (ingest → process → store → visualize), the difference between a data warehouse and a data lake, and ETL/ELT.
Analytics flows as collect, prepare, store, and present. Understanding this pipeline shows where each Azure service fits.
4.1.1The analytics pipeline
- Ingest: collect data from various sources.
- Process: clean and transform the data.
- Store: keep it in a warehouse or lake.
- Visualize: present it in reports and dashboards.
The pipeline starts from varied data sources (operational DBs, logs, IoT, external APIs) and ends in reports and dashboards people view. In "process," cleansing fixes missing or inconsistent values and transformation reshapes data for analysis. Keep the big picture: data flows from OLTP (operational systems, Chapter 1) through this pipeline into OLAP (the analytics platform).
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