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Komprise Adds Virtual Data Lake Capability to Reduce Cost of Finding Data for Analysis

Intelligent Data Management 2.11 focuses data search and indexing capabilities on finding relevant data stored across multiple platforms for big data projects.

Komprise, Inc. announced availability of its 2.11 release which includes a new Deep Analytics feature that addresses the biggest concern with big data analytics – searching across multiple storage platforms to identify the right data sets to analyze.

On average, 80% of the time IT teams spend on big data projects is related to identifying relevant data for analysis.

The new functionality, Deep Analytics, uses company’s data search and indexing technology to automate the process of finding unstructured data that fits specific criteria across disparate storage platforms and then creates virtual data lakes for analytics projects.

“It’s like finding a needle in a haystack in minutes,” said, David-Kenneth R. Turner, manager, information technology, Northwestern University. “Unstructured data is often made up of billions of files across millions of directories and finding the right data can be virtually impossible. With Komprise Deep Analytics, we can now find the data we need in minutes. For example, if we required all data created by ex-employees, or all files related to a specific project, we are able to operate on it as if it is a distinct entity, even if the data is residing in different storage solutions from multiple vendors and clouds.”

The core Komprise Intelligent Data Management software analyses data usage across storage and then automates the movement of data between platforms to put the right data in the right place at the right time. For example, the firm enables organizations to move cold or rarely used data to lower-cost storage, such as the cloud, without changing the experience of internal users. In this way customers are able to reduce storage and backup costs by up to 70% on implementation and then continue to optimize storage decisions for new data streams based on actual not perceived usage.

Deep Analytics works on top of this distributed index of files. With support for both standard and extended metadata or custom tags, customers can find data that fits criteria they set regardless of where the data actually lives, and export this virtual data lake to any analytics application or destination of their choice, such as Hadoop or Amazon Lambda. The resulting data set can be operated on as a discrete entity – all the permissions, access control, security and metadata is kept intact as this data lake moves.

“The launch of Komprise Deep Analytics extends our commitment to help customers to manage the exponential growth of data in a way that minimises costs and adds greatest value to the business,” comments Kumar Goswami, CEO Komprise. “Many of the end-users we talk to are experiencing an increase in requests from the business for historical data for analytics projects and becoming frustrated by the length of time it is taking to identify and extract the relevant data. In early customer trials, Komprise Deep Analytics is reducing the time to value for Big Data projects by 60%.”

Deep Analytics is an add-on component that can be deployed in the cloud or on-premises. The cloud version is a managed solution that can be turned on with the click of a button.

Additionally, it can be scaled on-demand, with no additional infrastructure or management needed. The cloud functionality is available, and the on-premises version will be available later this year.

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