GridGain Introduces New In-Memory Data Fabric to Support the Real-Time Enterprise

GridGain Systems, a provider of open source in-memory computing solutions, has launched the GridGain In-Memory Data Fabric. The new software solution enables high-performance transactions, real-time streaming and fast analytics in a scalable data access and processing layer. The goal of the In-Memory Data Fabric  is to offer a strategic approach to in-memory computing, while reducing the cost and complexity of deploying high-performance data processing.

According to the company, the GridGain In-Memory Data Fabric is designed to serve the emerging needs of the real-time enterprise, which does not view datasets as historical events, but instead as actionable intelligence for immediate decision-making, and as a way to deal with changing market forces. Although analytics and transactional data functions have traditionally operated separately, companies now need to view them as part of the same "data fabric," and should implement strategies that are able to connect enterprise-wide data from all sources. In particular, GridGain expects the solution to serve the needs of companies operating in an environment where predictive analytics is becoming a competitive necessity.

Built  from the ground up, the GridGain In-Memory Data Fabric is designed to support both existing and new applications in a distributed, massively parallel architecture on affordable commodity hardware.  The solution provides extensive API-parity across all key types of applications (Java, .NET, C++, Hive, MapReduce), connects applications with any traditional and emerging data stores (SQL, NoSQL, Hadoop), and, offers a secure and manageable data processing environment.

The compamy says it has already been adopted by  by Fortune 500 companies, top government agencies as well as innovative mobile and web companies for a range of business scenarios, includingfraud detection, big data analytics, and bioinformatics,

The GridGain In-Memory Data Fabric v6.5 is available for download at