9.2 — Event Streams Architecture

Event Streams operate on three layers. Understanding the architecture is what separates a working streaming pipeline from a fragile one.

1. Ingestion Layer

Sources that feed into Event Streams:

• IoT devices
• APIs
• Applications
• Databases
• Azure Event Hubs
• Message queues
• Log streams

2. Transformation Layer

Real‑time transformations applied in-flight — before data lands anywhere:

• Filtering — drop events that don’t match criteria
• Enrichment — join with reference data
• Mapping — rename and reformat fields
• Aggregation — compute running totals and windows
• Routing — send different events to different destinations

3. Output Layer

Event Streams can write to multiple destinations simultaneously:

• Lakehouses (Bronze layer)
• Fabric Warehouses
• KQL databases
• Power BI streaming datasets
• External systems

This three-layer architecture makes Event Streams a complete real‑time pipeline — from raw event at the edge to governed data in OneLake, with transformations applied along the way.