MapReduce was invented by Google in 2004, made into the Hadoop open source project by Yahoo! in 2007, and now is being used increasingly as a massively parallel data processing engine for Big Data.

Google and its MapReduce framework may rule the roost when it comes to massive-scale data processing, but there’s still plenty of that goodness to go around. This article gets you started with Hadoop, ...

Google announced on Wednesday that the company is open sourcing a MapReduce framework that will let users run native C and C++ code in their Hadoop environments. Depending on how much traction ...

When your data and work grow, and you still want to produce results in a timely manner, you start to think big. Your one beefy server reaches its limits. You need a way to spread your work across many ...

Google introduced the MapReduce algorithm to perform massively parallel processing of very large data sets using clusters of commodity hardware. MapReduce is a core Google technology and key to ...

You’ll want to be familiar with the Apache Hadoop framework before you jump into Elastic MapReduce. It doesn’t take long to get the hang of it, though. Most developers can have a MapReduce application ...

This implementation is intended for illustration purposes only and the examples lack exception handling acceptable for production systems. Beyond showcasing an implementation of the MapReduce concept, ...

In the vast universe of IT, data is categorized as being either structured or unstructured, from a macro perspective. Generation of unstructured data is orders of magnitude higher than that generated ...

In my last post, I explained MapReduce in terms of a hypothetical exercise: counting up all the smartphones in the Empire State Building. My idea was to have the fire wardens count up the number of ...