High-performance NoSQL databases and next-generation data analytics have converged in a recent deal between DataStax and Databricks. The companies have announced collaboration around the open source Apache Spark and Apache Cassandra platforms that they say will dramatically speed Big Data analytics.

The deal combines the massively scalable NoSQL database developed by DataStax, which is based on Cassandra, with the data-analytics platform from Databricks, itself founded by the creators of Spark. Together, the companies aim to provide an integrated data storage and analytics solution that will be up to 100 times faster than legacy platforms, according to a statement.

For now, hard numbers have not yet emerged to support that promise, nor is it yet clear exactly what kind of integrated solution the partners will offer. But the partnership does appear well-positioned at the confluence of two rapidly growing segments of the Big Data world, as more enterprises switch to NoSQL-type databases such as DataStax's for their storage needs and next-generation analytics toolsets such as Spark allow enterprises to analyze and derive value from that data.

Of course, the successful implementation of the type of data storage and analytics platform that DataStax and Databricks envision depends on enough enterprises deploying Spark instead of, or in addition to, Apache Hadoop, another analytics platform for Big Data. So far, Hadoop has generated bigger buzz, and is probably enjoying wider adoption, than Spark.

That said, Spark and Hadoop are not necessarily competitors, since each one is best suited to different types of situations. That means enterprises may choose to deploy the solution from Databricks and DataStax alongside a Hadoop-based platform if they like. And the fact that Spark is not yet quite as mature as Hadoop means there is a greater potential for growth within the Spark niche, making this a particularly good time for companies such as DataStax and Databricks to begin working on ways to deliver the power of Spark to the channel.