Google's MapReduce framework handles massive data sets in the blink of an eye. Lucky for you, it's possible to harness similar power for your own distributed data processing needs, with the open ...

Hadoop MapReduce has been widely embraced for analyzing large, static data sets. New technology integrates a stand-alone MapReduce engine into an in-memory data grid, enabling real-time analytics on ...

At data marketplace Infochimps, a DataRush user site, the company is using the software in a pilot effort to run Hadoop programs.

Hadoop 2.0 makes MapReduce less compulsory and the distributed file system more reliable.

Hadoop, of course, is the open-source framework for running applications on large data clusters built on commodity hardware (let's just say it: Big Data). I sometimes forget that Hadoop is actually a ...

That being the case, I'm going to assume that you've already built a Hadoop cluster (check out the last article if you need help with that), and I'll show you how to use a hive script to process data.

RainStor Big Data Analytics on Hadoop is not a connector – the database runs natively on top of Hadoop. Because it runs on the Hadoop stack, there’s no need to pipe data in and out of the ...

One of the side effects of the massive run-up in Hadoop deployments is the creation of a big skills shortage. IBM claims to have addressed part of that shortage with the general availability of an all ...