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, ...

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.

I gave an introductory talk on Hadoop yesterday at the Visual Studio Live! conference in Las Vegas. During the talk, I discussed how Hadoop Streaming, a utility which allows arbitrary executables to ...

Hadoop has been widely embraced for its ability to economically store and analyze large data sets. Using parallel computing techniques like MapReduce, Hadoop can reduce long computation times to hours ...

Two Google Fellows just published a paper in the latest issue of Communications of the ACM about MapReduce, the parallel programming model used to process more than 20 petabytes of data every day on ...

Reporting and analysis tools help businesses make better quality decisions faster. The source of information that enables these decisions is data. There are broadly two types of data: structured and ...

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The market for software related to the Hadoop and MapReduce programming frameworks for large-scale data analysis will jump from US$77 million in 2011 to $812.8 million in 2016, a compound annual ...

Hadoop is the most significant concrete technology behind the so called “Big Data” revolution. Hadoop combines an economical model for storing massive quantities of data – the Hadoop Distributed File ...