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In-Memory Computing Key to Digital Transformation

 “In-memory computing solutions empower organizations with the speed and scalability they need to deliver real-time, massively scalable applications for use cases in financial services, fintech, IoT, software, SaaS, retail, healthcare and more,” says Terry Erisman, Vice President of Marketing at GridGain Systems (
“It’s imperative for decision makers to understand the uses and benefits of the solutions available today, the potential of new solutions coming to market in the near term, and how these solutions can be optimized to meet the requirements of specific applications.”
This year’s In-Memory Computing Summit Europe, ( is scheduled for June 25 and 26, 2018 in London, and may hold the key to how organizations can meet the complex competitive challenges of today’s digital business transformation and omnichannel customer experience initiatives.
“The most important in-memory computing solutions to pay attention to today include In-memory data grids deployed on a cluster of on-premises, cloud, or hybrid servers as a simple and cost-effective way to speed up and scale out existing architectures to support increasingly data-intensive applications; In-memory databases, which are typically used when re-architecting existing data-intensive applications or building new ones; and Stream processing solutions, which take advantage of in-memory computing to manage the complexity around dataflow and event processing, making it easy for users to query active data without impacting performance,” says Erisman.
Additional areas under discussion at the Summit will include: In-memory computing platforms, which combine an in-memory data grid, an in-memory database, and stream processing – and can include continuous learning capabilities – to provide a single platform that significantly reduces development time, complexity and cost; Memory-centric architectures based on persistent data stores that use distributed ACID and ANSI-99 SQL-compliant disk storage to allow organizations to adjust the amount of data kept in-memory to achieve an optimal trade-off between infrastructure costs and application performance; and Machine learning (ML) and deep learning (DL) capabilities, which make it possible to greatly accelerate large-scale machine learning and deep learning use cases by running ML or DL algorithms directly against petabyte-scale operational datasets in real-time – without needing to move data into a separate modeling database. These solutions offer the potential of powering in-process HTAP applications, in which learning is continuous as models are updated as new transactional data is added to the operational data store.
The In-Memory Computing Summit Europe agenda ( includes speakers from companies such as Oracle, Intel, NEC Corporation of America, GridGain Systems, Hazelcast, iguazio, Neeve Research, ScaleOut Software, Software AG,, and VoltDB. Hundreds of industry enthusiasts will gather to learn, network with their fellow attendees, and share their experiences with in-memory computing.

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