Azure Stream Analytics

Real-time analytics on fast-moving streaming data.


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.

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Date News
Welcome back to another episode of Armchair Architects as part of the Azure Enablement Show. Today we will be discussing how do you get meaning from your data? Our hosts will be David...
New features are now available in Stream Analytics no-code editor GA including Power BI output support, and data preview optimization. Power BI output feature enables you to build real-time...
The team catches up with April Edwards to learn about the benefits of using Bicep to deploy Azure resources.   Media...
Stream Analytics no-code editor now supports capturing Event Hubs data into ADLS gen2 with Delta Lake format.
New features are now available in Stream Analytics no-code editor GA including multiple parameter built-in functions support, Delta Lake format support in ADLS Gen2 output sink.
The processor diagram in physical job diagram provides you further insights within the streaming nodes of your stream analytics jobs.
Stream Analytics now supports end-to-end exactly once semantics when writing to Azure Data Lake Storage Gen2.
Azure Stream Analytics is a fully managed, real-time analytics service designed to help you analyze and process fast moving streams of data.
The job diagram simulator provides a capability to visualize your Stream Analytics job’s topology and help you improve the query’s parallelism as you develop your streaming query.
Stream Analytics no-code editor enables you to develop a Stream Analytics job in minutes with drag and drop experience. Now, it is generally available with several new capabilities added.
Azure Stream Analytics currently allows you to use user-assigned managed identities to authenticate your job's inputs and outputs.
You can use managed identity to authenticate to your Cosmos DB output from Azure Stream Analytics.
Azure Data Explorer output from Azure Stream Analytics is now generally available.
The physical job diagram provides rich, instant insights to your Stream Analytics job to help you quickly identify the causes of problems when you troubleshoot issues.
You can now configure a Stream Analytics job to write streaming data to either a new or an existing delta lake yable in Azure Data Lake Storage Gen2.
Your Stream Analytics jobs get up to 45% performance boost in CPU utilization by default.
You can now connect your Stream Analytics jobs running on a dedicated cluster to your synapse dedicated SQL pool using managed private endpoints.
Native output connector for Azure Database for PostgreSQL allows you to easily build real time applications with the database of your choice.
Authenticate your Stream Analytics jobs to connect to Service Bus using system-assigned managed identities.
New features are now available in Stream Analytics no-code editor public preview including Azure SQL database available as reference data input and output sink, diagnostic logs available for...