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Memory is the Hidden Secret to Success with Big Data: GridGain’s In-Memory Hadoop Accelerator Login

Memory is the Hidden Secret to Success with Big Data: GridGain’s In-Memory Hadoop Accelerator

Two big trends are driving IT today. One, of course, is big data. The growth in big data IT is tremendous, both in terms of data and in number of analytical apps developing in new architectures like Hadoop. The second is the well-documented long-term trend for critical resources like CPU and memory to get cheaper and denser over time. It seems a happy circumstance that these two trends accommodate each other to some extend; as data sets grow, resources are also growing. It's not surprising to see traditional scale-up databases with new in-memory options coming to the broader market for moderately-sized structured databases. What is not so obvious is that today an in-memory scale-out grid can cost-effectively accelerate both larger scale databases as well as those new big data analytical applications.

A robust in-memory distributed grid combines the speed of memory with massive horizontal scale-out and enterprise features previously reserved for disk-orienting systems. By transitioning data processing onto what's really now an in-memory data management platform, performance can be competitively accelerated across the board for all applications and all data types. For example, GridGain's In-Memory Computing Platform can functionally replace both slower disk-based SQL databases and accelerate unstructured big data processing to the point where formerly "batch" Hadoop-based apps can handle both streaming data and interactive analysis.

While IT shops may be generally familiar with traditional in-memory databases - and IT resource economics are shifting rapidly in favor of in-memory options - less known about how an in-memory approach is a game-changing enabler to big data efforts. In this report, we'll first briefly examine Hadoop and it's fundamental building blocks to see why high performance big data projects, those that are more interactive, real-time, streaming, and operationally focused have needed to continue to look for yet newer solutions. Then, much like the best in-memory database solutions, we'll see how GridGain's In-Memory Hadoop Accelerator can simply "plug-and-play" into Hadoop, immediately and transparently accelerating big data analysis by orders of magnitude. We'll finish by evaluating GridGain's enterprise robustness, performance and scalability, and consider how it enables a whole new set of competitive solutions unavailable over native databases and batch-style Hadoop.

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Publish date: 07/08/14

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