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Technical Insight: Converged, Real-time Analytics

Published February 11, 2015. John Webster explores the impact of real-time analytics on in-memory computing. GridGain Data Fabric is used as the primary example. Download the free report now!

Converged, Real-time Analytics: Enabling Faster Decision Making and New Business Opportunities

Executive Summary

An expanding use of analytics tools that can converge and extract value from multiple data sources has fueled a growing interest in real-­time information delivery. Business executives are discovering that information gleaned from real-­time data sources—both internal and external to the enterprise—can have more relevance and value in a competitive business context than information that essentially looks backward in time. In addition, the fact that results can be had with greater speed means that more business can be generated on a daily basis vs. using traditional business intelligence system architectures.

The power of new in-­memory data processing technologies becomes apparent when business users can be offered new real-­time analytics services that can be used to guide them to gain competitive advantage on an ongoing basis. This approach makes in-­memory independent of specific vendor-­centric solutions (SAP HANA, for example), alleviating the need to buy, train for, and support different ones from each vendor as needs arise.

An example of this approach can be found in GridGain’s In-­Memory Data Fabric, a software-­only implementation that is available on the open source model or in an enterprise edition. Here we review the GridGain Data Fabric architectural approach as a way to extract value from multiple data sources and in real time.

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