The no-code editor is now available in Azure Stream Analytics portal to enable building your Stream Analytics jobs effortlessly, no coding required.
Are you using GitHub for managing your Stream Analytics project and looking to leverage GitHub's powerful CI/CD pipeline? Follow this comprehensive guide and learn to set up a CI/CD pipeline…
Azure Stream Analytics supports for end-to-end exactly once semantics when writing to Azure Data Lake Storage Gen2.
You can connect directly to Kafka to ingest from Kafka clusters or output data to Kafka clusters in your Azure Stream Analytics (ASA) job.
Stream Analytics now supports end-to-end exactly once semantics when writing to Event Hub output.
Azure Stream Analytics can now fetch schema from the Schema Registry and deserialize data from Event Hub inputs for Avro format.
You can connect your Azure Stream Analytics job to Azure Data Explorer / Kusto clusters using managed private endpoints
Use Stream Analytics to process exported data from Application Insights
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 dashboard…
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 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.
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.
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 troubleshooting, and…
Explore four new features in the no code editor in Azure Event Hubs. This editor allows you to easily develop a Stream Analytics job without writing a single line of…
You can now configure your Azure Stream Analytics job to write to a SQL table that hasn't yet been created or see schema mismatch detection for an existing SQL table.
Azure Stream Analytics is a fully managed, real-time analytics service designed to help you analyze and process fast moving streams of data.
Autoscaling allows you to define the minimum and maximum streaming units. Stream Analytics will automatically take care of dynamically optimizing the number of streaming units needed for your workload.
Maximum size of a Stream Analytics job and a cluster is increased from 192 SUs to 396 SUs.
See improvements to the Stream Analytics query development experience on the Azure portal to increase productivity.
Stream Analytics no code editor provides a rich, no code, drag and drop experience for you to build you streaming pipeline within minutes.
Stream Analytics now supports authenticating to Azure Cosmos DB and Azure Service Bus using Managed Identities.
Azure Stream Analytics is a fully managed, real-time analytics service designed to help you analyze and process fast moving streams of data.
You can now use user-assigned managed identity to authenticate your Stream Analytics jobs to inputs and outputs without ever having to worry about credential management.
Machine Learning user-defined function in Stream Analytics allows you to perform high throughput, low latency, real-time predictions, allowing you to act on insights which have a very short shelf-life.
We are announcing that Azure Stream Analytics can directly output data to Azure Data Explorer, simplifying architecture where you need both hot and warm path analytics on streaming data. This…
Azure Availability Zones enabled Stream Analytics allows you to run mission-critical applications with higher availability and fault tolerance to datacenter failures.
Azure Stream Analytics is a fully managed, real-time analytics service designed to help you analyze and process fast moving streams of data that can be used to get insights, build…
Azure Stream Analytics is a fully managed, real-time analytics service designed to help you analyze and process fast moving streams of data that can be used to get insights, build…
Azure Stream Analytics Tools for Visual Studio Code is for developers to easily author, test, debug, and manage Azure Stream Analytics jobs.
Azure Stream Analytics is a fully managed, real-time analytics service designed to help you analyze and process fast moving streams of data that can be used to get insights, build…
Azure Stream Analytics Dedicated offers a single-tenant clustered deployment of Stream Analytics for complex and demanding streaming scenarios.
Azure Stream Analytics now supports managed identity for Blob input, Event Hubs (input and output), Synapse SQL Pools and customer storage account. As a result, customers do not have to…
The latest update of Azure Stream Analytics, now deployed in every supported region, provides support for larger reference data ( up to 5 GB), and add support for composite keys…
The latest update of Azure Stream Analytics includes extended SQL language operators such as new windowing and analytics functions; and improvements of existing features.
The Azure Stream Analytics CI/CD tools are now updated with query unit testing capability. Users can now add and run automated test cases for their queries.
With a brand new output connector to Synapse SQL Pools, Stream Analytics can now support throughput rates even higher than 200MB/sec while ensuring ultra-low latencies.
Azure Stream Analytics clusters delivers support for Azure Virtual Network (VNet) and more predictable performance.