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ScaleOut StateServer 5.0 Introduces Cloud-ready Performance and Scalability Breakthroughs for Enterprise Applications

By SD Times Newswire

February 15, 2012 — (Page 1 of 2)
ScaleOut Software, a leading provider of in-memory data grids (IMDGs), today announced the immediate availability of ScaleOut StateServer Version 5.0 beta. IMDGs provide distributed, in-memory storage which scales application performance and enables powerful query and analysis of stored data. Version 5.0 introduces an innovative new architecture designed to run in cloud infrastructures and easily scale to hundreds of servers. This version also adds the ability to transparently combine multiple IMDGs into a single virtual data grid and other key innovations.

"Distributed, in-memory data grids are being rapidly adopted for both on-premise and cloud use," said Dr. William L. Bain, founder and CEO of ScaleOut Software. "Enterprise applications increasingly require immediate access to data globally from a variety of sources, and developers are faced with the challenge of how to share information across logical and geographical boundaries. Addressing these challenges is one of the focus areas for Version 5.0."

With its ScaleOut GeoServer option, Version 5.0 introduces a full range of features for multi-site integration. In addition to asynchronous data replication for disaster recovery, GeoServer now lets users combine in-memory data grids at multiple sites into a single, logically coherent, virtual grid, enabling seamless data access across sites without the need to explicitly track which site is hosting requested data. This breakthrough capability saves both development time and resources at a time when IT head count and budgets continue to be under scrutiny. This helps increase both efficiency and productivity across the enterprise.

With the enhanced feature set in ScaleOut AnalyticsServer (formerly Grid Computing Edition), Version 5.0 enables in-memory data grids to offer industry-leading capabilities and performance in parallel query and data analysis. AnalyticsServer now supports optimized parallel query of stored data based on Java or C# object properties, and it fully supports the Microsoft Language Integrated Query (LINQ) standard. These capabilities have been integrated into its proven map/reduce engine to provide a powerful platform for memory-based data analysis. Its intuitive, object-oriented map/reduce model saves development time, and its fast, memory-based analysis dramatically lowers latency in comparison to other map/reduce platforms.

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