MapR Aims to Advance Data-Intensive Applications and In-Place Analytics

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The enterprise software company touts ‘new innovations’ in MapR DB that bring together operational, analytical and real-time applications at global scale.

MapR Aims to Advance Rich Data-Intensive Applications and In-Place Analytics

(Source: MapR Technologies.)

Enterprise software platform provider MapR Technologies announced  at the Strata Data Conference today new database features including capabilities that enable rich applications, in-place and continuous machine learning/AI, new SQL capabilities, and global real-time data integration, according to the company.

Designed to bring together operational, analytical and real-time applications, the new release is designed to include powerful and efficient data access with native secondary indexes, and rich app development with an OJAI 2.0 API, according to the company.

[ Related: Finding Value With Edge Computing: Q&A With MapR’s Jack Norris ]

Other features include in-place self-service SQL data exploration and operational BI with optimized drill integration, real-time processing and machine learning with native Spark and Hive connectivity, and real-time application integration with global change data capture.

MapR_DB can now handle a much broader set of applications, including Internet of Things, single view, personalization, metadata catalogs, artificial intelligence/machine learning applications.

MapR-DB can operate thousands of mission critical applications in a multi-tenant environment, according to Jack Norris, senior vice president for Data and Applications at MapR. Business can add more intelligence to their applications to build in real-time features such as recommendations engines, security features to recognize and previous patterns, said Norris.

Scalability and reliability

MapR Converged Data Platforms one platform architecture offers high scalability, 99.999 reliability and increased performance. MapR-DB capabilities extend from on-premises to the cloud to the edit from the cloud to the edge and on-premises, Norris said.

[ Related: Scaling SQL for Time-Series Data in IoT Use Cases ]

“Retro-fitting legacy databases in big data environments presents challenges such as poor data performance, scale limitations, and data access restrictions, said John L. Myers, managing research director, Enterprise Management Associates, a Boulder, Colo.-based analyst firm. “Leveraging a one platform architecture, MapR Converged Data Platform enables organizations to efficiently process all data types bringing analytics and operations together with consistent scale, reliability and performance.”

MapR 6.0 with new MapR-DB features is scheduled for release in the fourth quarter.

Dan Muse

About Dan Muse

Dan Muse is the former editor in chief of CIO.com. He has covered technology for three decades and held senior editorial positions with Ziff Davis, Jupitermedia, Disney Publishing, McGraw-Hill and Advance Digital.

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