GridGain Experts Share In-Memory Computing Strategies, Provide Practical Advice at Worldwide Virtual Events

GridGain Systems, provider of enterprise-grade in-memory computing solutions based on Apache Ignite,  announced it is hosting or participating in multiple conferences, webinars, and virtual meetups taking place in June 2020 to share their unique insight into the latest in-memory computing technology developments and trends.

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“GridGain experts have helped hundreds of companies deploy in-memory computing technologies to successfully achieve highly performant, massively scalable applications that leverage digital integration hubs, HTAP and other strategies,” said Terry Erisman, executive vice president of marketing and alliances at GridGain Systems. “Our virtual events are great opportunities for attendees to connect with our experts, learn about the in-memory computing strategies that may be best for them, and hear practical implementation advice.”

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GridGain Systems is revolutionizing real-time data access and processing by offering an in-memory computing platform built on Apache^® Ignite. Common use cases for the GridGain platform include application acceleration and as a digital integration hub for real-time data access across data sources and applications. GridGain solutions are used by global enterprises in financial services, software, e-commerce, retail, online business services, healthcare, telecom, transportation and other major sectors, with a client list that includes ING, Raymond James, American Express, Société Générale, Finastra, IHS Markit, ServiceNow, Marketo, RingCentral, American Airlines, Agilent, and UnitedHealthcare. GridGain delivers unprecedented speed, massive scalability, and real-time data access for both legacy and greenfield applications. Deployed on a distributed cluster of commodity servers, GridGain software can reside between the application and data layers (RDBMS, NoSQL and Apache^® Hadoop^®), requiring no rip-and-replace of the existing databases, or it can be deployed as an in-memory database.

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