Appfluent Announces New Version of Leading Big Data Analytics Solution

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Appfluent Visibility version 6.1 provides powerful data insights via a single user interface.

Appfluent is the only company to completely analyze how data is used across heterogeneous analytic database systems.

Appfluent Technology, Inc., a provider of Big Data analytic solutions, today announced the latest release of the company’s industry leading product. Featuring a powerful new interface, Appfluent Visibility Version 6.1 provides IT management with an extensive view across multiple Big Data platforms.

Visibility Version 6.1 has the ability to monitor and analyze data usage within Teradata, Oracle/Exadata, IBM DB2, IBM® PureData™ for Analytics, powered by Netezza and Hadoop fromone user interface. IT organizations and business users gain powerful dashboards that uncover the entire ‘who, what, when and where’ of their data. The intelligence gained can be used to overcome data warehouse capacity and performance constraints, drive data warehouse and BI optimization, and pave the way for migration, audit and compliance initiatives.

“Appfluent is the only company that can completely analyze how data is used across organizations’ heterogeneous analytic database systems,” said Frank Gelbart, Chief Executive Officer of Appfluent. “Our solution provides the information and history organizations need to maximize the return on their big data investments and manage continually rising IT expenditures.”

About Appfluent

Appfluent provides IT organizations with unprecedented visibility into their Big Data systems to reduce costs. Appfluent helps companies put the right workload on the right system, across data warehouses, business intelligence, and Hadoop. Seeing every analytic and Extract, Transform and Load (ETL) that hits your Big Data systems, Appfluent can see where you are wasting expensive compute and storage. With Appfluent, enterprises can address exploding data growth with confidence, proactively manage performance of BI and data warehouse systems, and realize the tremendous economies of Hadoop.

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