Azure Stream Analytics now supports MATCH_RECOGNIZE. MATCH_RECOGNIZE in Azure Stream Analytics significantly reduces the complexity and cost associated with building, modifying, and maintaining queries that match sequence of events for alerts or further data computation.
Azure Stream Analytics supports managed identity authentication with egress to Azure Blob Storage. The identity is a managed application registered in Azure Active Directory that represents a given Stream Analytics job, and can be used to authenticate to a targeted resource. Managed identities eliminate the limitations of user-based authentication methods, like needing to reauthenticate due to password changes or user token expirations that occur every 90 days.
Stream Analytics now offers native support for Apache Parquet format when writing to Blob storage. Apache Parquet is a columnar storage format tailored for bulk processing and query processing in the big data ecosystems.
Visual Studio's April 2019 update includes new features for Stream Analytics including local testing, new stream analytics features (like blob output partitioning) and added intellisense for things like anomaly detection.
Azure Stream Analytics now offers full support for WKT geospatial format, opening new opportunities for developers. In addition, Stream Analytics added support for geospatial index of reference data, providing faster processing to support, for example, much larger scale fleet management and connected cars scenarios. Transitioning from geographic-based to geometry-based calculations will deliver geo-projected results to end users.
Stream Analytics now empowers every developer to easily add anomaly detection capabilities to their Stream Analytics jobs without requiring them to develop and train their own machine learning models. Ready-to-use machine learning models are provided right within the SQL language. This reduces the cost and complexity associated with building and training machine learning models to a simple single function call.
Azure Stream Analytics now supports custom deserializers. Be it Parquet, Protobuf, XML, or any binary format, developers can implement custom deserializers in C#, and then use them to deserialize events received by Stream Analytics.
Four new built-in functions in Azure Stream Analytics address common scenarios:
REPLACE replaces all occurrences of a specified string value within another string value.
COALESCE evaluates a list of arguments and returns the value of the first expression that is not NULL.
ROUND returns a numeric value rounded to the specified length or precision.
CROSS JOIN joins reference data with an event stream.