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Compatibility level 1.2 for Azure Stream Analytics jobs is now available.
The MATCH_RECOGNIZE function is now available in Stream Analytics.
Easily add anomaly detection capabilities to your Stream Analytics jobs without the requirement to develop and train your own machine learning models. Ready-to-use unsupervised learning ML models are provided within the SQL language.
Azure Stream Analytics now supports high-performance, real-time scoring by taking advantage of custom pre-trained machine learning models that are managed by Azure Machine Learning service and hosted in Azure Kubernetes Service (AKS) or Azure Container Instances, using a workflow that doesn't require users to write any code.
Stream Analytics now offers full support for managed identity-based authentication with Power BI for dynamic dashboarding experience.
Take advantage of the power of Stream Analytics to process data in Protobuf, XML, or any custom format.
Target availability: Q4 2019
With online scaling capability, it’s now possible to increase or decrease the SU capacity of a running job without having to stop it, so you will no longer be required to stop your job if you need to change the SU allocation.
Developers creating Stream Analytics modules in the cloud or on IoT Edge can now write or reuse custom C# functions and invoke them right in the query through user-defined functions.
Customers can now use Azure Stream Analytics in the France Central region.
Azure Stream Analytics is now available in the Korea Central region.
Eligible customers can now use Azure Stream Analytics in the US GOV Virginia region.
Target availability: Q4 2019
It's now possible to configure an Azure SQL Database Managed Instance or even a SQL server running on a virtual machine as a reference data input for you Stream Analytics job.
It’s now possible to configure an Azure SQL Database Managed Instance or even a SQL server running on a virtual machine as an output in your Stream Analytics job.
With just 1 click, Event Hubs customers can easily visualize incoming streaming data and start writing Stream Analytics query from the Event Hubs portal.
With the new MATCH_RECOGNIZE function, developers can easily define event patterns using regular expressions and aggregate methods to verify and extract values from the match.
Azure Stream Analytics now offers output adapter for egress to Azure Data Lake Storage Gen 2.
Azure Stream Analytics now offers native support for Apache Parquet format when writing to Azure Blob storage or Azure Data Lake Storage Gen 2.
Developers can now use aggregates such as SUM, COUNT, AVG, MIN and MAX directly with the OVER clause, without having to define a window.
Now you can authenticate Stream Analytics egress to Azure Blob Storage using managed identities.
Stream Analytics now offers native support for Apache Parquet format when writing to Azure Blob storage.
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