Serverless service that processes streaming data with a SQL-like language, offering windowing functions and anomaly detection out of the box.
Read more: Microsoft Learn
In the Ultra Transcenders books
Each book explains Azure Stream Analytics in context, with comparison tables and the common traps.
Terms in this definition
- Serverless
Compute tier for single Azure SQL databases that scales automatically, pauses when idle and charges by the second. It is offered in General Purpose and Hyperscale, not Business Critical, and reserved capacity does not apply.
- Streaming data
Events that keep arriving without end, for example log entries, sensor readings or social media posts; stream processing is how they are handled.
- Anomaly detection
Identifying data points or time-series values that look out of the ordinary, such as fraud or a spike from a sensor. Microsoft retired the dedicated Azure AI Anomaly Detector service on 1 October 2026.
Related terms
- Micro-batch
Rather than one record at a time, Azure Stream Analytics and Spark Structured Streaming process incoming streamed records in small groups as they arrive; each group is a micro-batch.
- Perpetual query
Unlike a one-off query, it keeps running against streamed events: picking out certain types, projecting values, or aggregating them per time window, for instance a per-minute count of sensor readings. Each Azure Stream Analytics job runs one between an input and an output.
- Streaming dataflows
Once offered no-code preparation and ingestion of streaming data in Power BI but has now been retired. Those capabilities live on in Azure Stream Analytics no-code stream processing.
- Streaming units
The unit by which compute for Azure Stream Analytics jobs is scaled.