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A Need for Speed: Integrating In-Memory with Stream Processing to More Effectively Tackle Big Data

White Paper

The term “big data” refers to data that is so large and processed so quickly that it is nearly impossible to analyze with traditional methods like on-premises, centralized databases. It has become a critical part of daily business operations due to its potential to solve problems that older forms of data analysis were previously unable to tackle.

The mismatch between businesses’ need for big data analysis and their inability to process big data on traditional systems has spurred the business industry’s exploration of new technologies that can more efficiently manage their ever-growing accumulations of data. One of the most effective ways to store big data is via an in-memory data store, or a type of database that keeps data entirely in the random-access memory (RAM) of a set of networked computers. The collective RAM of an in-memory data store is ideal for large applications that are too big for the RAM of a single computer to handle, making the interconnected database an ideal repository for specific subsets of your big data.

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