Continuing its drive to bring the batch-oriented Hadoop Big Data platform into the real-time world, MapR technologies, Inc. today is announcing a host of new features designed to support on-the-fly decision-making. MapR 5.0 is also crafted to support bigger workloads, responding to what MapR says is a trend toward customers running more applications on individual clusters.

The new release automatically synchronizes storage, database and search indices for real-time transactions and includes improved security auditing, an area that is considered to be a MapR forte. Release 5.0 also adds support for Apache Drill 1.0 and the latest 2.7 release of Hadoop and YARN.

 MapR is also continuing its push to make Hadoop clusters easier to configure with the addition of auto-provisioning templates that use a wizard-like format to create clusters according to the most common configuration options. They can be used, for example, to provision data lakes with services deployed in a typical Hadoop cluster, or alternatively for schema-free interactive exploration using Apache Drill. MapR said the templates automate layout, and server provisioning while also executing a suite of tests to ensure that the template deployments will perform as expected.

MapR also aims to position its Hadoop distribution as a real-time data transport layer between multiple data stores, including relational DBMS, network-attached storage, HBase, and data processing engines like Spark. Drill 1.0 adds self-service data exploration, and Spark 1.3 integration provides for rapid application development and execution. The combination of JSON and Drill, in particular, position MapR as a source for end-user data exploration across multiple back ends. “From the beginning we’ve focused on eliminating the batch limitations of Hadoop,” he said.

 Version 5.0 of the MapR Distribution will be available in 30 days.

Source: http://siliconangle.com