MapR collaborates with Waterline on data catalog for machine learning-driven AI and analytics; speeds data classification, removes downtime

MapR Technologies announced this week the availability of a machine learning-based (ML) data catalog with the MapR Data Platform. The new Waterline Data Catalog for MapR, a result of development collaboration and a reseller agreement with Waterline Data, is designed to address the speed and scale required for enterprise-wide data governance and management for next-generation Artificial Intelligence (AI) and analytics environments.

The age of AI and analytics requires robust, automatic data governance tooling for big data given that critical business decisions are being made against data that must be searchable, high-quality and instantly identifiable.

To achieve a high-level of confidence in the data, the integration of the MapR and Waterline Data solution considers more than a single environment by spanning disparate systems across the enterprise.

The Waterline Data Catalog for MapR provides enterprise coverage using a full set of technologies supporting platform-based data security, data tagging, data rating, data lineage, catalog searching, data dictionary, and data lifecycle management for all data residing inside or outside of the big data platform.

“MapR and Waterline Data are a powerful combination to speed the impact of ML and AI use cases,” said Anil Gadre, executive vice president of product management, MapR.  “With MapR’s out-of-the-box security designed to maintain a strong line of defense in the protection of data, and Waterline’s smart Data Catalog using Artificial Intelligence, the combined solution brings a complete set of data governance technologies to the enterprise.”

“Competing in today’s Data Economy requires putting your data to work with speed and accuracy,” said Waterline Data CEO Kailash Ambwani. “With the Waterline Data Catalog for MapR, organizations looking to leverage a modern big data architecture can automate the discovery, classification and governance of their data at scale without compromising security. Organizations that do not embrace machine learning in this manner will continue to drown in their own data, unable to act on information they don’t know they have.”

 


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