GridGain Systems Introduces In-Memory Computing Solutions Deployed on Microsoft Azure to Address the Needs of the Financial Services Industry

FOSTER CITY, CA--(Marketwired - Sep 13, 2016) - GridGain Systems, provider of enterprise-grade In-Memory Computing solutions based on #Apache ® #Ignite™, announced today that they are now offering the GridGain In-Memory Data Fabric deployed on #Microsoft #Azure. The newly available GridGain on Azure will help financial services organizations leverage Microsoft's integrated cloud services to rapidly and effectively deploy GridGain's distributed, massively parallel, in-memory solution. Based on Apache Ignite, the GridGain In-Memory Data Fabric enables massive scale out of data-intensive applications and a 1,000x improvement in transaction times versus disk-based approaches without replacing the existing underlying databases. It provides high-speed transactions with #ACID guarantees, real-time streaming, and fast analytics in a single, comprehensive data access and processing layer. GridGain powers existing or new applications in a distributed, massively parallel architecture on affordable, industry-standard hardware, which can be easily scaled by adding more nodes to the compute grid. The GridGain In-Memory Data Fabric requires minimal or no modifications to the application or database layers for architectures built on #RDBMS, #NoSQL or Apache™ #Hadoop® databases. "The release of GridGain's In-Memory Data Fabric on #Microsoft #Azure is an important step in addressing the needs of our rapidly expanding customer base," said Abe Kleinfeld, President and CEO of GridGain. "In particular, we're seeing broad adoption of GridGain by top tier banks and financial services firms who are in turn moving quickly to the cloud. Those users will now have a reliable, high performance platform on which to easily deploy GridGain solutions for OLTP, OLAP or hybrid OLTP/OLAP use cases. With the flexibility of Azure, our customers will have options whether they are outsourcing all of their server environment or simply offloading peak compute workload on demand."
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