Ingest Real-Time Data with Eventstreams in Microsoft Fabric
Ingest Real-Time Data with Eventstreams in Microsoft Fabric
Source
- Provider: Microsoft
- Platform: Microsoft Learn
- Source title: Ingest real-time data with Eventstreams in Microsoft Fabric
- Source URL: https://learn.microsoft.com/en-us/training/modules/ingest-eventstreams-fabric/
- Content type: Self-Paced Training Module
- Target Exam: Exam DP-700
- Date captured: 2026-08-15
- Last reviewed: 2026-08-15
Summary
This module covers configuring and routing streaming data with Microsoft Fabric Eventstreams, applying no-code event processing transformations (filtering, aggregations, windowing), and landing streams into multiple destinations.
Streaming Sources & Transformations
1. Supported Eventstream Sources
- Custom App: Push events via REST API or Kafka protocol endpoints.
- Azure Event Hubs & Azure IoT Hub: Direct connection to enterprise telemetry event brokers.
- Amazon Kinesis Data Streams: Multi-cloud streaming ingestion.
- Azure SQL Database CDC: Streaming change-data-capture logs.
2. In-Stream Processing Operators
- Filter: Drop irrelevant events before landing.
- Manage Fields: Rename, cast, or drop JSON attributes.
- Aggregate: Tumbling, hopping, and sliding window aggregations over streaming time windows.
- Union & Expand: Flatten nested JSON arrays and combine event streams.
3. Eventstream Destinations
- KQL Database / Eventhouse: Sub-second indexing for real-time querying.
- Lakehouse: Land stream directly into Delta tables in OneLake (Bronze layer).
- Custom Endpoint / Reflex: Trigger automated alerts or actions based on streaming conditions via Data Activator.
Exam Traps & Gotchas
[!IMPORTANT]
- Enhanced Capabilities Mode: When creating Eventstreams, ensure Enhanced Capabilities is enabled to support multiple destinations and rich stream transformations.
- Direct Ingestion to Lakehouse: Eventstreams can write directly to Lakehouse Delta tables without requiring an intermediary Spark notebook.