New analytics functions for stream processing

  • Design matching:

    Now generally available (GA), this feature offers out-of-the-box support for Azure SQL Database as it pertains information input. This includes the capability to automatically refresh your benchmark dataset periodically. Also, to carry on the performance of your Stream Analytics job, we give the choice to fetch incremental adjustments from the Azure SQL Database by composing a delta query. Ultimately, Stream Analytics leverages versioning of reference information to augment streaming data with the reference information that was legitimate at the time the event was generated. This guarantees repeatability of results.

  • Use of analytics operate as aggregate:

Egress to Azure Data Lake Storage Gen2


Azure Stream Analytics offers full support for Managed Identity based authentication using Azure Blob Storage on the output side. Customers can continue to use the connection string based authentication model. This feature is available as a general preview.

Reference data is a slow or static shifting dataset utilized to fortify real-time information flows to provide greater contextual insights. An example situation would be currency exchange rates often updated to reflect market trends, then converting a flow of billing events in different currencies to a frequent currency of choice.
A number of these features simply began rolling out worldwide and will be available in most regions within several weeks.
Azure Stream Analytics is a totally managed PaaS offering that enables real-time data and complex event processing on quickly moving data flows. As a result of zero-code integration with over 15 Azure solutions, developers and data engineers can easily build complex pipelines for hot-path analytics in a few minutes. Today, at Inspire, we’re announcing various new innovations in Stream Analytics which assist further decrease time to respect for solutions that are powered by real time insights.
Native support for egress from Apache parquet structure into Azure Blob Storage is now generally available. Parquet is a columnar format enabling efficient big data processing. By outputting information in parquet format into a blob store or a information lake, you can make the most of Azure Stream Analytics to power large scale streaming infusion, move, and load (ETL), to conduct batch processing, to train machine learning algorithms, or even to run interactive queries on your historic data. We are currently announcing general availability of the feature for egress to Azure Blob Storage.

  • Managed identities (formerly MSI) authentication:

    Today, we’re announcing unmarried integration with Occasion Hubs. Available as an individual preview feature, this allows an Event Hubs customer to picture incoming data and begin to compose a Stream Analytics query with one click in the Event Hub portal. When the query is ready, they’ll be able to operationalize it in few clicks and begin devoting actual time insights. This will significantly decrease the time and cost to create real-time analytics solutions.

    Augmenting streaming data using SQL reference information support

    Enhancements into blob output

    • Native support for Apache parquet arrangement:

      The Azure Stream Analytics team is extraordinarily committed to listening to your comments and allowing the consumer voice affect our potential investments. We welcome you to join the conversation and make your voice heard through our UserVoice webpage .
      Now you can use aggregates such as SUM, COUNT, AVG, MIN, and MAX right using the OVER clause, without having to define a window. Analytics acts as Aggregates enables users to easily express queries like “Why is the latest temperature higher than the maximum temperature reported at the last 24 hours? ”
      With the new MATCH_RECOGNIZE feature, it is possible to easily define event patterns using regular expressions and innovative methods to verify and extract values from the match. This allows you to easily express and operate complex event processing (CEP) in your streams of information. For example, this function will make it possible for users to easily author a question to discover “head and shoulder” patterns onto the on a stock market feed.
      Azure Stream Analytics is a central component within the Big Data analytics pipelines of Azure clients. While Stream Analytics concentrates on the real time or hot-path analytics, services such as Azure Data Lake help empower batch processing and advanced machine learning. Azure Data Lake Storage Gen2 takes core capabilities from Azure Data Lake Storage Gen1 such as a Hadoop compatible file system, Azure Active Directory, and POSIX based ACLs and incorporates them into Azure Blob Storage. This combination allows best in class analytics functionality together with storage tiering and data lifecycle management capabilities along with the fundamental availability, safety, and durability capacities of Azure Storage.

      Bringing the power of real time insights to Azure Event Hubs customers