Teradata and MapR Partner on Hadoop-Based Big Data AnalyticsTeradata and MapR Partner on Hadoop-Based Big Data Analytics
MapR has moved to marry its Apache Hadoop distribution with Big Data analytics and exploration features that far exceed those available in the basic open source Hadoop platform through a major new partnership with Teradata, which will become a reseller of MapR software and services.
November 19, 2014
MapR has moved to marry its Apache Hadoop distribution with big data analytics and exploration features that far exceed those available in the basic open source Hadoop platform through a major new partnership with Teradata, which will become a reseller of MapR software and services.
The partnership, announced Nov. 19, means Teradata will integrate MapR's Hadoop distribution, which is among the most popular iterations of the open source big data platform that are currently available, into the Teradata Unified Data Architecture platform.
By extension, the partnership agreement "also provides for Teradata to resell MapR software, professional services and customer support and serve as the single point of contact for customers that use both Teradata and MapR solutions," MapR indicated in a statement, adding, "Orchestration capabilities such as Teradata QueryGrid and Teradata Loom will be directly integrated with MapR software."
The goal of the partnership, according to the companies, is to offer more choice in the Hadoop market "by providing integration, joint product development, and unified go-to-market strategy."
The big data world has seen plenty of partnerships between Hadoop distributors and data analytics vendors already, but this move is interesting because of the wholesale integration of MapR Hadoop and Teradata Unified Data Architecture that it involves. The companies' platforms will not just complement one another, but have become mutually dependent. That could be an important step in driving Hadoop adoption forward, since it provides a one-stop big data solution that eliminates the need for separately orchestrating different components of the big data infrastructure.
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