Welcome to Azure Stream Analytics

Azure Stream Analytics is a fully managed stream processing engine that is designed to analyze and process large volumes of streaming data with sub-millisecond latencies. Patterns and relationships can be identified in data that originates from a variety of input sources including applications, devices, sensors, clickstreams, and social media feeds. These patterns can be used to trigger actions and initiate workflows such as creating alerts, feeding information to a reporting tool, or storing transformed data for later use. Stream Analytics is also available on the Azure IoT Edge runtime, enabling to process data directly on IoT devices.

The following scenarios are examples of when you can use Azure Stream Analytics:

  • Streaming ETL pipeline to Azure Storage in Parquet format
  • Event driven applications with Azure SQL Database and Azure Cosmos DB
  • Analyze real-time telemetry streams and logs from applications and IoT devices
  • Real-time dashboarding with Power BI
  • Anomaly detection to detect spikes, dips, and slow positive and negative changes in sensor values
  • Geospatial analytics for fleet management and driverless vehicles
  • Remote monitoring and predictive maintenance of high value assets
  • Clickstream analytics to determine customer behavior

You can try Azure Stream Analytics with a free Azure subscription.

Stream Analytics intro pipeline

Key capabilities and benefits

Ease of use

Azure Stream Analytics is easy to start. It only takes a few clicks to connect to multiple sources and sinks, creating an end-to-end pipeline. Stream Analytics can connect to Azure Event Hubs and Azure IoT Hub for streaming data ingestion, as well as Azure Blob storage to ingest historical data. Job input can also include static or slow-changing reference data from Azure Blob storage or SQL Database that you can join to streaming data to perform lookup operations.

Stream Analytics can route job output to many storage systems such as Azure Blob storage, Azure SQL Database, Azure Data Lake Store, and Azure Cosmos DB. You can also run batch analytics on stream outputs with Azure Synapse Analytics or HDInsight, or you can send the output to another service, like Event Hubs for consumption or Power BI for real-time visualization. For the entire list of Stream Analytics outputs, see Understand outputs from Azure Stream Analytics.

The Azure Stream Analytics no-code editor offers a no-code experience that enables you to develop Stream Analytics jobs effortlessly, using drag-and-drop functionality, without having to write any code. It further simplifies Stream Analytics job development experience. To learn more about the no-code editor, see No-code stream processing in Azure Stream Analytics

Programmer productivity

Azure Stream Analytics uses a SQL query language that has been augmented with powerful temporal constraints to analyze data in motion. You can also create jobs by using developer tools like Azure PowerShell, Azure CLI, Stream Analytics Visual Studio tools, the Stream Analytics Visual Studio Code extension, or Azure Resource Manager templates. Using developer tools allows you to develop transformation queries offline and use the CI/CD pipeline to submit jobs to Azure.

The Stream Analytics query language allows you to perform CEP (Complex Event Processing) by offering a wide array of functions for analyzing streaming data. This query language supports simple data manipulation, aggregation and analytics functions, geospatial functions, pattern matching and anomaly detection. You can edit queries in the portal or using our development tools, and test them using sample data that is extracted from a live stream.

You can extend the capabilities of the query language by defining and invoking additional functions. You can define function calls in the Azure Machine Learning to take advantage of Azure Machine Learning solutions, and integrate JavaScript or C# user-defined functions (UDFs) or user-defined aggregates to perform complex calculations as part a Stream Analytics query.

Fully managed

Azure Stream Analytics is a fully managed (PaaS) offering on Azure. You don't have to provision any hardware or infrastructure, update OS or software. Azure Stream Analytics fully manages your job, so you can focus on your business logic and not on the infrastructure.

Run in the cloud or on the intelligent edge

Azure Stream Analytics can run in the cloud, for large-scale analytics, or run on IoT Edge or Azure Stack for ultra-low latency analytics. Azure Stream Analytics uses the same tools and query language on both cloud and the edge, enabling developers to build truly hybrid architectures for stream processing.

Low total cost of ownership

As a cloud service, Stream Analytics is optimized for cost. There are no upfront costs involved - you only pay for the streaming units you consume. There is no commitment or cluster provisioning required, and you can scale the job up or down based on your business needs.

Mission-critical ready

Azure Stream Analytics is available across multiple regions worldwide and is designed to run mission-critical workloads by supporting reliability, security, and compliance requirements.


Azure Stream Analytics guarantees exactly once event processing and at-least-once delivery of events, so events are never lost. Exactly once processing is guaranteed with selected output as described in Event Delivery Guarantees.

Azure Stream Analytics has built-in recovery capabilities in case the delivery of an event fails. Stream Analytics also provides built-in checkpoints to maintain the state of your job and provides repeatable results.

As a managed service, Stream Analytics guarantees event processing with a 99.9% availability at a minute level of granularity.


In terms of security, Azure Stream Analytics encrypts all incoming and outgoing communications and supports TLS 1.2. Built-in checkpoints are also encrypted. Stream Analytics doesn't store the incoming data since all processing is done in-memory. Stream Analytics also supports Azure Virtual Networks (VNET) when running a job in a Stream Analytics Cluster.


Stream Analytics can process millions of events every second and it can deliver results with ultra low latencies. It allows you to scale out to adjust to your workloads. Stream Analytics supports higher performance by partitioning, allowing complex queries to be parallelized and executed on multiple streaming nodes. Azure Stream Analytics is built on Trill, a high-performance in-memory streaming analytics engine developed in collaboration with Microsoft Research.

Next steps

You now have an overview of Azure Stream Analytics. Next, you can dive deep and create your first Stream Analytics job: