Are streaming big data analytics ready for prime time? StreamAnalytix, which has expanded its open source analytics platform to include support for Apache Spark Streaming, thinks so. And there is a lesson here for the big data ecosystem generally.
As its name implies, StreamAnalytix focuses on real-time data analytics. Its product portfolio previously centered on Apache Storm, an open source platform for real-time distributed data processing.
With the addition of Spark Streaming support, StreamAnalytix now offers an enterprise-level solution for processing streaming data as well. That means handling the types of continuous, rapidly flowing data generated by things like IoT devices and hardware sensors.
StreamAnalytix is only one of a number of companies and organizations working in this area. But it hopes to stand out by focusing on solutions that are based on open source tools (even if they are not themselves open source). The Spark Streaming support makes StreamAnalytix's platform "the industry’s first open-source based, enterprise-grade, multi-engine platform for rapid and easy development of real-time streaming analytics applications," the company says.
From a channel perspective, the company's decision to add Spark Streaming support to its portfolio is significant because it highlights streaming data's emergence as an increasingly important part of the big data scene. Distributed data analytics via platforms like MapReduce have been around for years. Real-time data analytics of the type handled by Apache Spark in general are also not new.
But the market is now introducing solutions that combine distributed and real-time analytics. That change makes it easier to process very large volumes of data, in real-time, in a highly scalable fashion.