With so many Apache Hadoop distributions on the market, what sets them apart? MapR is hoping that bringing feature-rich open source searching to its Big Data platform via integration with Elasticsearch will help draw users by making Big Data analytics and business intelligence faster and more comprehensive.

Search features are certainly not new to Hadoop, but Elasticsearch aims to innovate by supporting scalable searches in real time across different types of data. It helps "Hadoop users enhance their workflows with a full-blown search engine that scales no matter the amount of data," according to Elasticsearch creator, founder, and CTO, Shay Banon.

When integrated into Mapr's Hadoop distribution, Elasticsearch provides developers with "a scalable, distributed architecture to quickly perform search and discovery across tremendous amounts of information," the two companies say. They report that the solution is already in use at "several Fortune 100 financial services institutions," as well as IT security and compliance vendor Solutionary.

Like Apache Hadoop, Elasticsearch is governed by the open source Apache 2.0 license. The first version of the software was released in 2010, and in 2012, Elasticsearch developers launched the eponymous technology to commercialize the project and guide further development. Elasticsearch currently receives nearly 500,000 downloads each month.

From the channel perspective, the most important part of this story is about the open source Hadoop Big Data world becoming an even more diverse ecosystem where solutions depend on collaboration between a variety of independent parties. Companies such as MapR have been repackaging the core Hadoop code and distributing it in value-added, enterprise-ready form for some time, but Elasticsearch integration into MapR is a sign that Hadoop distributions also need to incorporate other open source Big Data technologies, which they do not build themselves, to maximize usability for the enterprise.