Vhayu and NetApp team on data storage

Source: Vhayu Technologies

Vhayu Technologies, the leading provider of enterprise tick data solutions, today announced membership in the NetApp (NASDAQ: NTAP) Partner Program.

Financial services providers use tick data, an amalgamation of price and volume data, to derive real-time analysis of the equities, futures, and options markets.

Vhayu is collaborating with NetApp to tightly integrate NetApp storage solutions into a Vhayu Velocity environment. With this partnership, customers will be able to cost effectively store and easily share tick data volumes in the multiterabyte range.

Vhayu's Velocity suite of products for the financial services industry contains high-performance market data solutions for the capture and high-speed analysis of massive amounts of streaming and historical data. A critical piece of Vhayu's solution is the ability to use fast and reliable shared storage to access historical archives of trading data. Through Vhayu's membership in the NetApp Partner Program, customers benefit from NetApp's unified storage solution, which is second to none in allowing storage access by using multiple protocols-including FC, iSCSI, NFS, and CIFS-in a single architecture.

"NetApp and Vhayu engineers recently completed an extensive project to demonstrate the suitability of NetApp storage for Vhayu Velocity implementations," said John Coulter, vice president of Corporate Strategy at Vhayu. "Overall, we found that NetApp FC SAN storage systems provided excellent performance when configured with the Vhayu applications. Together, the solution offers customers a proven option for the storage of historical data and archive access."

"The NetApp Partner Program brings cost-effective, validated solutions to our customers that speed time to market," said Patrick Rogers, vice president of Solutions Marketing at NetApp. "Through this partnership, NetApp storage systems and NetApp FlexClone® technology will help Vhayu's customers deploy Velocity in a multiterabyte data environment while maximizing storage efficiency and minimizing deployment time."

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