Streaming Analytics at the edge and in the cloud SQLstream Blaze + Amazon Kinesis Analytics

"The store-before-query analytics and conventional ETL models are irrelevant in a world where streaming analytics can empower businesses to take the next right action, continuously and real time. Now that Amazon Web Servies has licensed Blaze technology, the combination of Amazon Kinesis Analytics and SQLstream Blaze makes it easier than ever for businesses to securely and cost-effectively ingest, analyze, and manage streaming data on and between public cloud, private cloud, and on premises.”

CEO, SQLstream


SQLstream Blaze is now powering Amazon Kinesis Analytics

IoT applications, mobile devices, wearables, industrial sensors, as well as many software applications and services can generate staggering amounts of streaming data – sometimes TBs per hour. Businesses need streaming analytics to ingest, analyze, and manage data as it arrives, to take the next right actions continuously and in real time. The combination of Amazon Kinesis Analytics and SQLstream Blaze offers the best streaming analytics solution for on-premises and cloud environments.

In 2016, SQLstream Blaze was licensed and implemented by AWS for its streaming analytics platform component, and is currently powering Amazon Kinesis Analytics using ANSI standard SQL.

In parallel, the SQLstream Blaze Adapter for Kinesis enables businesses to securely and cost-effectively ingest, analyze, and manage streaming data on and between cloud and on-premises environments.

The Blaze Adapter is available in SQLstream Blaze – try it for free today.


  • Easily ingest streaming data within cloud and on-premises environments

    Discovery and automatic ingest and transform any data format – structured or unstructured, live or historical, in motion or at rest – from or to any data format, interfacing with a wide array of sources and destinations including Amazon Kinesis and Firehose, Hadoop, data warehouses, message buses (including Kafka), files, and devices.

  • Easily ingest all data into and out of AWS

    Streaming ingestion allows data to be moved continuously and in real time from data sources, to the cloud, and from the cloud through a high performance bi-directional SQLstream Blaze Kinesis Adapter.

  • Painlessly operationalize insights continuously and in real time

    Analysis of high-volume, high-velocity data, turning it into actionable information with millisecond latency using SQLstream Blaze technology licensed by AWS. Live data streams are integrated and analyzed with historical data for accurate business context, record by record, eliminating the risk of incomplete analysis for time sensitive decisions, due to batch or micro-batch processing.

  • Deploy real-time analytics at the edge for a far lower cost

    Developing streaming applications and deploying them for optimal performance near data sources at the edge, on premises, or in the cloud. Declarative SQL allows automatic optimization and parallelization, dramatically reducing runtime footprint and hardware requirements. Streaming analytics enables data to be analyzed and acted on as the data arrive, without needing to store, eliminating unnecessary security risks and storage costs.

  • Centralize streaming data visibility and security at global scale

    Global view of data flowing through deployments using telemetry provided by Blaze. Data can be managed and marshalled for storage or analyzed without storing, for optimal value and security.

  • Use existing apps, databases, data warehouses, and skillsets

    Using data from existing sources and writing standard SQL queries on streaming data, without having to learn any new programming skills. In addition, the SQLstream development environment, StreamLab, enables data analysts and SQL, Java, Python, and Scala developers to build streaming applications in minutes.

  • Enjoy zero downtime

    Maintain continuous business operations while changing streaming application logic or queries, on-the-fly, while data is in motion.

  • Automate accurate, contextual actions

    Operationalizing actions with applications that deliver triggers and alerts based on real-time, record-by-record contextual analysis of live and historical data, including time-series and spatial operators.