# Azure Stream Analytics

> Managed SQL stream processing that also runs on IoT Edge

Azure Stream Analytics is a fully managed stream processing engine using a SQL dialect extended with temporal constraints, running in the cloud or on IoT Edge with the same tools and query language.

Website: https://azure.microsoft.com/en-us/products/stream-analytics

## Use it when

- The stack is Azure and the job is SQL: Event Hubs or IoT Hub in, Synapse, Cosmos DB, Functions, or Power BI out, with a live dashboard as a native sink.
- The same logic must run at the edge; the identical query language and tooling deploy to IoT Edge containers next to the devices.
- Nobody wants to size a cluster; jobs are billed per streaming unit and can be resized while running.
- Analysts should participate; a no-code editor sits beside the query language, and JavaScript UDFs extend it.

## Think twice when

- Sources or sinks live outside Azure; connectivity is almost exclusively Azure services, with Kafka input the only outsider and its output still in preview.
- State is large or long-lived; the service manages state in memory with temporal state capped at seven days.
- The logic outgrows SQL; there is no code-level API, and complex jobs migrate to Flink or Dataflow-shaped engines.

## How it runs

Fully managed: write the query, attach inputs and outputs, choose streaming units; the service (built on the Trill engine) checkpoints, recovers, and guarantees exactly-once processing with at-least-once delivery to most sinks. The same job ships as an IoT Edge container for on-premises execution.

## Details

### General

| Attribute | Value |
| --- | --- |
| License | Commercial |
| Deployment | Managed, Hybrid |
| Workload | Streaming |
| Operational complexity | Low |
| Pricing | Subscription |
| Documentation | https://learn.microsoft.com/en-us/azure/stream-analytics/ |

### Stream Processing

| Attribute | Value |
| --- | --- |
| Processing model | Fully managed continuous SQL over in-memory state; watermark progress is the only output trigger, so there are no partial aggregates |
| APIs | SQL-like query language; no-code editor; JavaScript UDFs and aggregates, Azure ML functions (C# retired in 2024) |
| What you run | Nothing: a managed Azure job sized in streaming units; the same engine ships as an IoT Edge container for on-premises jobs |
| Connectors | Azure services almost exclusively: Event Hubs, IoT Hub, Blob and ADLS Gen2 in; Synapse, Data Explorer, Cosmos DB, SQL, PostgreSQL, Power BI, Functions, Service Bus and Table storage out. Kafka is the only outside input and its output is still preview |
| Windowing | Tumbling, hopping, sliding, session, snapshot |
| Event time and lateness | Out-of-order and late arrival tolerances are configurable, each set to drop or adjust; events over five minutes early are always dropped; substreams give a watermark per key |
| State backend | Managed by the service and held in memory, with temporal state capped at seven days; checkpoints and replay recovery |
| Delivery guarantee | Exactly-once processing; At-least-once delivery to most sinks |
| Reading results | Through outputs; Power BI is a native one, so a dashboard can be the sink |
| Scaling | Streaming units can be changed while the job runs, though a non-partitioned output or mismatched PARTITION BY steps restrict which values are available |

## Capabilities

- [Stream Processing](https://matca.io/capabilities/stream-processing)

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Source: https://matca.io/azure-stream-analytics
Last updated: 2026-08-31T11:40:48.871Z