Azure Stream Analytics preview features
This article summarizes all the features currently in preview for Azure Stream Analytics. Using preview features in a production environment isn't recommended.
The following features are in public preview. You can take advantage of these features today, but don't use them in your production environment.
Authenticate to SQL Database output with managed identities
Azure Stream Analytics supports Managed Identity authentication for Azure SQL Database output sinks. Managed identities eliminate the limitations of user-based authentication methods, like the need to reauthenticate due to password changes or user token expirations that occur every 90 days. When you remove the need to manually authenticate, your Stream Analytics deployments can be fully automated.
Output to Azure Synapse Analytics
Azure Stream Analytics jobs can output to a SQL pool table in Azure Synapse Analytics and can process throughput rates up to 200MB/sec. This supports the most demanding real-time analytics and hot-path data processing needs for workloads such as reporting and dashboarding.
With online scaling, you are not required to stop your job if you need to change the SU allocation. You can increase or decrease the SU capacity of a running job without having to stop it. This builds on the customer promise of long-running mission-critical pipelines that Stream Analytics offers today. For more information, see Configure Azure Stream Analytics Streaming Units.
C# custom de-serializers
Developers can leverage the power of Azure Stream Analytics to process data in Protobuf, XML, or any custom format. You can implement custom de-serializers in C#, which can then be used to de-serialize events received by Azure Stream Analytics.
Extensibility with C# custom code
Developers creating Stream Analytics modules in the cloud or on IoT Edge can write or reuse custom C# functions and invoke them directly in the query through user-defined functions.
Debug query steps in Visual Studio
You can easily preview the intermediate row set on a data diagram when doing local testing in Azure Stream Analytics tools for Visual Studio.
Local testing with live data in Visual Studio Code
You can test your queries against live data on your local machine before submitting the job to Azure. Each testing iteration takes less than two to three seconds on average, resulting in a very efficient development process.
Visual Studio Code for Azure Stream Analytics
Azure Stream Analytics jobs can be authored in Visual Studio Code. See our VS Code getting started tutorial.
Real-time high performance scoring with custom ML models managed by Azure Machine Learning
Azure Stream Analytics supports high-performance, real-time scoring by leveraging custom pre-trained Machine Learning models managed by Azure Machine Learning, and hosted in Azure Kubernetes Service (AKS) or Azure Container Instances (ACI), using a workflow that does not require you to write code. Sign up for preview
Live data testing in Visual Studio
Visual Studio tools for Azure Stream Analytics enhance the local testing feature that allows you to test you queries against live event streams from cloud sources such as Event Hub or IoT hub. Learn how to Test live data locally using Azure Stream Analytics tools for Visual Studio.
.NET user-defined functions on IoT Edge
With .NET standard user-defined functions, you can run .NET Standard code as part of your streaming pipeline. You can create simple C# classes or import full project and libraries. Full authoring and debugging experience is supported in Visual Studio. For more information, visit Develop .NET Standard user-defined functions for Azure Stream Analytics Edge jobs.
The following features are also available in preview on request.
Support for Azure Stack
This feature enabled on the Azure IoT Edge runtime, leverages custom Azure Stack features, such as native support for local inputs and outputs running on Azure Stack (for example Event Hubs, IoT Hub, Blob Storage). This new integration enables you to build hybrid architectures that can analyze your data close to where it is generated, lowering latency and maximizing insights. This feature enables you to build hybrid architectures that can analyze your data close to where it is generated, lowering latency and maximizing insights. You must sign up for this preview.