MapR, one of the more prominent providers of commercial distributions of Apache Hadoop, has added native support for JSON documents to MapR-DB, its NoSQL database.
The addition gives MapR’s enterprise Hadoop platform the ability to run analytics on data stored in one of the most used data-transfer formats today. MapR’s distribution provides both Hadoop, for batch analytics, and Spark for real-time, or stream, analytics.
JSON, or JavaScript Object Notation, is primarily a standard browsers and servers use to talk to each other. It is an alternative data-transfer format to XML. Like XML, it is text-only, but it’s simpler and easier to use. It generally takes fewer lines of code to describe an object in JSON than it does in XML.
JSON is also a more elegant alternative to storing data in a relational database. For example, it can aggregate data from a row that spans 20 relational database tables into a single document, or object, according to a white paper by Couchbase.
One big practical advantage of adding native JSON support to MapR’s enterprise Hadoop platform is the ability to run analytics on data wherever it resides instead of extracting it to one place, where an analytics application can crunch through it, Jack Norris, chief marketing officer at MapR, said. “You can do that now in an integrated environment,” he said.
MapR can run analytics on JSON documents without big data transfers and as the documents themselves change. “The types of applications that are possible are basically moving from a reporting and a ‘what happened’ perspective to integrating analytics and impacting the business as it’s happening,” Norris said.
Those applications can be things like making a split-second user identity decision after a credit card is swiped, making personalized shopping recommendations in real time, or customizing ads for millions of cable subscribers based on demographics and viewing habits.
Another advantage is scalability. According to Norris, a scalable system using JSON has traditionally been an “oxymoron,” but because MapR has put a lot of time and resources into making sure its platform scales well in one or across multiple data centers, the document standard’s scalability problems have been addressed, he said.
MapR promises scalability across thousands of nodes per cluster, with clusters deployed in data centers around the world.
The company is targeting a general-availability release of the new features in the next quarter. A developer preview is available for download now.
