And still, up to 80% of time spent on stream processing goes NOT to value-adding operations or money-yielding apps, but on data wrangling.

SQLstream’s efforts to streamline data wrangling as an integral part of streaming analytics awarded us 5/5 scores for data connectors, transformation operators, and correlation operators in the prestigious The Forrester Wave™: Streaming Analytics, Q3 2017

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Continuous Discovery & Ingestion



SQLstream Blaze simplifies and accelerates data ingest for all formats. There is no need to hand-code data pipelines or re-create low-level code.  The batch-oriented limitations of Big Data processing and traditional ETL are removed. All data formats including log files, web logs, sensor or IoT data, machine data, and CDC from databases, may be ingested, filtered, parsed, and simultaneously enriched with structured, stored data, from any or many sources for analysis.


data sources 

Streaming Data Integration



Machine data from log files, sensor networks, and applications contains a wealth of information most companies can’t use, because they can’t translate formats into a common language. But if you can’t see it, you can’t use it. 

SQLstream Blaze integrates all data in real time, to help you make decisions based on customer and consumer behavior, location, QoS/QoE, financial transactions, security and compliance breaches, the state of your industrial processes, transportation networks, equipment health, and so on.


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Data Enrichment

SQLstream Blaze can re-stream historical data and integrate it with the raw streams before running queries, for a complete and more accurate business context.




Better than Lambda

SQLstream Blaze is the only stream analytics platform that enriches raw data as it arrives with reference data from any or multiple databases in true real time. Unstructured, structured, local, distributed, in-motion and at-rest data may be integrated and analyzed continuously and at rates of millions of records per second per core. Our bidirectional read/write/update connections enable:

  • A stable, unified architecture model where both streaming and historical data are processed in one layer, continuously and in real time.
  • Fast scenario testing.
  • Concurrent dataload for any number of different Big Data, RDBMS and EDW platforms, from the same input data.
  • Seamless additions of new Big Data platforms, such as Google BigQuery.
  • Kafka implementations through built-in plugins for Kafka & AMQP as source and sink.


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SQLstream Blaze is free to download and use up to 1GB/day (after which it throttles).