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Imanis Data Releases Enhanced Machine Learning Data Management Functionality

By   /  June 29, 2018  /  No Comments

A recent press release reports, “Imanis Data, the machine learning data management company for the data driven world, today announced a significant upgrade to its Imanis Data Management Platform (IDMP). As organizations increasingly rely upon distributed data platforms including Cassandra, MongoDB, and HortonWorks, the latest release delivers important capabilities including enhanced ThreatSensetm ransomware detection, disaster recovery for Hadoop, and new rapid recovery testing. ‘Organizations are challenged by unprecedented data growth and an increasing need to harness the value of their data, all while protecting themselves against cyberattacks and ensuring compliance requirements are met,’ said Peter Smails, CMO, Imanis Data. ‘Imanis Data has deployed groundbreaking machine learning functionality at the core of our platform to help organizations manage the complexity and scale of their Big Data environments’.”

The release goes on, “Capabilities of the latest release include: (1) Enhanced Ransomware Detection – ThreatSense, Imanis’s machine learning-powered anomaly detection software, has been enhanced with 2x the number of metrics used to build the baseline data patterns that future data points are validated against. The increased number of baseline metrics result in higher granularity anomaly detection and fewer false positive indications. (2) Disaster Recovery for Hadoop – a new data mirroring feature allows customers to automatically create periodic and incremental copies of Hadoop databases from one data center to another, or cloud enabling them to rapidly recover in the event of a data center outage. (3) Rapid Recovery Testing – as a best practice, administrators need to periodically test their backups to ensure they are restorable, but the associated time and cost can be prohibitive. The new Recovery Sandbox feature allows administrators to test recoverability quickly on the IDMP with no disruption to production workloads.”

Read more at Business Wire.

Photo credit: Imanis Data

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