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Using an In-Memory Data Grid for Near Real-Time Data Analysis | HPCWire

by Dr. William Bain, ScaleOut Software, Inc.


In today’s competitive world, businesses need to make fast decisions to respond to changing market conditions and to maintain a competitive edge. The explosion of data that must be analyzed to find trends or hidden insights intensifies this challenge. Both the private and public sectors are turning to parallel computing techniques, such as "map/reduce" to quickly sift through large data volumes.

In some cases, it is practical to analyze huge sets of historical, disk-based data over the course of minutes or hours using batch processing platforms such as Hadoop. For example, risk modeling to optimize the handling of insurance claims potentially needs to analyze billions of records and tens of terabytes of data. However, many applications need to continuously analyze relatively small but fast-changing data sets measured in the hundreds of gigabytes and reaching into terabytes.  Examples include clickstream data to optimize online promotions, stock trading data to implement trading strategies, machine log data to tune manufacturing processes, smart grid data, and many more.

Over the last several years, in-memory data grids (IMDGs) have proven their value in storing fast-changing application data and scaling application performance.  More recently, IMDGs have integrated map/reduce analytics into the grid to achieve powerful, easy-to-use analysis and enable near real-time decision making. For example, the following diagram illustrates an IMDG used to store and analyze incoming streams of market and news data to help generate alerts and strategies for optimizing financial operations. This article explains how using an IMDG with integrated map/reduce capabilities can simplify data analysis and provide important competitive advantages.

Real-Time Analytics Engine 


What is an In-Memory Data Grid?

By storing fast-changing data within a middleware software tier, IMDGs enable applications to seamlessly scale performance by adding servers that access and update a shared, memory-based data set.  To maximize scalability, IMDGs automatically load-balance data across servers on which the grid is hosted. They also redundantly store data on multiple servers to ensure high availability in case a server or network link fails. Additional capabilities, including eventing and distributed locking, make IMDGs a powerful data storage platform.

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