GridGain Announces Next Version of In-Memory Data Fabric with Enhanced Security, Availability, Management and Performance
Foster City, California – October 14, 2015 – GridGain Systems, the leading innovator behind open source and commercial in-memory data fabric solutions that accelerate business operations and time to insights for the enterprise and in the cloud, announced today the newest release of its award-winning In-Memory Data Fabric Powered by Apache Ignite™. This is the first GridGain Enterprise Edition release since Apache Ignite became a top-level project in August, and is available for immediate download on the company’s website. The latest release, v7.4, of GridGain In-Memory Data Fabric includes enhancements to security, management, availability and performance.
The GridGain In-Memory Data Fabric is a proven enterprise-grade software solution that delivers unprecedented speed and unlimited scale to accelerate business and time to insights. It enables high-performance transactions, real-time streaming and fast analytics in a single, comprehensive data access and processing layer. It powers both existing and new applications in a distributed, massively parallel architecture on affordable, industry-standard hardware.
The GridGain solution is based on Apache Ignite™ 1.4, the first major Ignite release since the project was graduated to top-level project by the ASF in August (http://j.mp/asfignite). The main focus of version 7.4 is enhanced security, availability and performance, and some of the new features include:
- Enhanced integration with Apache YARN™ and Apache Mesos™: Enhances management and enables users to utilize either tool for job scheduling.
- Faster JDBC driver implementation: Improves performance for applications that require standard SQL access to the GridGain In-Memory Data Fabric.
- Secure Socket Layer (SSL) support: Provides an encrypted link between server and client, allowing sensitive information to be transmitted securely for protected communication and discovery.
- Support for Log4j2™: This logging tool is a beneficial component of the development cycle and provides useful insight on errors and rich auditing.
"Given the tremendous success of Apache Ignite and its swift graduation to ASF top level project status, it's clear that the in-memory data fabric is achieving mainstream adoption due to its ability to help organizations compete more effectively," said Abe Kleinfeld, President and CEO of GridGain. "With this newest release of GridGain In-Memory Data Fabric, we're continuing to satisfy customers' growing demands, including delivering enhanced security, performance, availability and management."
This release of GridGain Enterprise Edition is available for download immediately on the GridGain website. The newest release of GridGain Community Edition is also available on the website.
GridGain is revolutionizing real-time data access and processing by offering the first enterprise-grade In-Memory Data Fabric powered by Apache Ignite™. Apache Ignite™ is a project by the Apache Software Foundation, which integrates a compute grid, data grid, service grid, as well as streaming, messaging and Hadoop acceleration into a single solution that connects traditional and emerging data stores (SQL, NoSQL, Hadoop) with cloud-scale applications, and enables massive data throughput and ultra-low latencies across any number of clustered commodity servers. The GridGain In-Memory Data Fabric is designed to conquer today’s Fast Data challenges and unleash the competitive advantage of any real-time business, whether on-premises or in the cloud. Offering the most comprehensive, enterprise-grade in-memory computing solution for high-volume transactions, real-time analytics and hybrid data processing, GridGain enables Fortune 500 companies and innovative mobile, web and SaaS companies to anticipate and innovate ahead of market changes. GridGain is headquartered in Foster City, California. Download a 30-day free trial of the GridGain In-Memory Data Fabric here. For more information, visit gridgain.com and follow us @GridGain.
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