MapR Unveils New Open Distribution for Hadoop and SQL Tools

San Jose, Calif.-based Apache Hadoop specialist MapR Technologies announced several new initiatives to establish itself as the “most open distribution” of Hadoop. Earlier this month MapR became a “solution provider” member of the Data Services Workgroup for the Open Data Center Alliance, which gives it the opportunity to “define enterprise requirements to address interoperability, security and manageability of Big Data frameworks.” MapR also announced the release of an Open Database Connectivity (ODBC) 3.52 driver as part of the MapR Distribution, allowing users to leverage hundreds of commercial and open source SQL-based tools, including query builders and BI applications such as Excel, Tableau, MicroStrategy and a variety of open-source SQL tools, like Kaimon.

“Unlike other Hadoop distributions, there is no need to deploy agents or specialized connectors to get data into and out of a Hadoop cluster,” said Tomer Shiran, director of product management at MapR Technologies, in the press release. “MapR opens up Hadoop by allowing users to leverage a broad array of standard commands, tools and applications.”

In addition, MapR announced full support for Linux Pluggable Authentication Modules (PAM), enabling MapR to validate users with common authentication back-ends, including Active Directory, LDAP, NIS, Kerberos and a variety of third-party services. Lastly, MapR announced the availability of source code for the various components of the MapR distribution. MapR has a broad set of open source components as part of its distribution including Cascading, Flume, HBase, Hive, Pig, Mahout, Oozie, Sqoop, Whirr and ZooKeeper. All of the source code, patches and updates are now available for these modules and users can make changes while using the underlying MapR storage and compute framework.

Two months ago MapR Technologies announced a “comprehensive” set of data connections for Apache Hadoop, meant to enable a “wide range of data ingress and egress alternatives for customers,” including direct file-based access using standard tools, direct database connectivity, Hadoop-specific connectors via Sqoop, Flume and Hive, and access to popular data warehouses and applications using custom connectors.

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