Energizing the elephant

ScaleOut hServer V2 adds its own shot of real-time analytics functionality to Hadoop MapReduce

Lucy Carey

New version promises enhanced speed and flexibility for Hadoop adherents.

In well under a decade, Hadoop has become the go-to solution for big data analytics, offering scalability, fault tolerance, and critically, mass accessibility. But it’s not a fix all – there are myriad uses and functionalities within the colossus of the framework yet to be unpacked. An increasing number of users are investigating software with the real-time analytics options which fresh out of the box Hadoop simply cannot currently offer, such as Storm. Yet, as the launch of hServer V2 by ScaleOut Software this week demonstrates, there’s a host of solutions out there ensuring Hadoop remains a force to be reckoned with.

ScaleOut Software’s hServer, an in-memory data grid for Hadoop MapReduce that first launched earlier this summer, works b y bypassing the batch-based framework and enabling Hadoop to work in a real-time manner, acting as the catalyst for a whole new wave of implementations.

With the addition of ScaleOut Software’s MapReduce engine, the company reckons that applications can run 20 times faster than with plain old Hadoop. It’s probably not ideal for titanic sized batches of data, but should be quite effective for analyzing real-time operational data, as well as testing and debugging small segments of full MapReduce workloads.

With Hadoop’s lack of speed a bugbear for many vendors, anyone who can conceivably offer a way to make the plodding pachyderm run a little swifter is guaranteed to make a profit – meaning there’s a raft of similar options out there. When we asked ScaleOut Software’s CEO Bill Bain what differentiates hServer V2 from the rest of the herd, he commented that, ScaleOut Software, “Have directly leveraged our deep in-memory parallel computing technology and customer experience to introduce an in-memory Hadoop MapReduce execution engine that is fully integrated with our in-memory data grid”.

According to Bain, this provides “a computing environment to run Hadoop MapReduce code on [with] fast-changing data sets”, and enables continuous analysis on live operational data with Hadoop MapReduce. He added that, “While there are other products that accelerate various aspects of Hadoop, our focus on in-memory computing puts us ahead of the curve in applying Hadoop to meet real-time needs”.

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