Today, we are announcing the general availability of Azure Stream Analytics (ASA) on IoT Edge, empowering developers to deploy near-real-time analytical intelligence closer to IoT devices, unlocking the full value of device-generated data. With this release, Azure Stream Analytics enables developers to build truly hybrid architectures for stream processing, where device-specific or site-specific analytics can run on containers on IoT Edge and complement large scale cross-devices analytics running in the cloud.
Azure Stream Analytics now supports high-performance and efficient write operations to Azure SQL Database and Azure SQL Data Warehouse to help customers achieve four to five times higher throughput than what was previously possible. To achieve fully parallel topologies, Stream Analytics will transition SQL writes from serial to parallel operations while simultaneously allowing for batch size customizations.
Visual Studio tooling for Azure Stream Analytics further enhances the local testing capability to help users test their queries against live data or event streams from cloud sources such as Azure Event Hubs or IoT Hub. This helps developers test their queries without having to stop and restart their jobs, so they're more efficient.
In Azure Stream Analytics, users can optionally specify the number of partitions of a stream when performing repartitioning. This will enable better performance tuning when the partition key can't be changed to upstream constraints, or there's a fixed number of partitions for output, or partitioned processing is needed to scale out to a larger processing load.
Developers who create Azure Stream Analytics modules for Azure IoT Edge can now write custom C# functions and invoke them right in the query through user-defined functions (UDFs). This enables scenarios like complex math calculations, importing custom machine learning models by using ML.NET, and programming custom data imputation logic.
This week at Microsoft Ignite 2018, we are excited to announce eight new features in Azure Stream Analytics (ASA). These new features include
Support for query extensibility with C# custom code in ASA jobs running on Azure IoT Edge.
Custom de-serializers in ASA jobs running on Azure IoT Edge.
Live data Testing in Visual Studio.
High throughput output to SQL.
ML based Anomaly Detection on IoT Edge.
Managed Identities for Azure Resources (formerly MSI) based authentication for egress to Azure Data Lake Storage Gen 1.
Blob output partitioning by custom date/time formats.
User defined custom re-partition count.
Today, we released version 1.16 of the Azure Management Libraries for Java. Now you can programmatically manage Azure Monitor using Azure Management Libraries for Java, specifically, you can:
Manage Diagnostic Settings
Stream logs and metrics to Event Hub, Storage Account or Log Analytics
Set up Metric Alerts
Query Activity Logs
Set up Activity Log Alerts
Setup Auto Scale
Perform Advanced Analytics