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.

Implemented Map Reduce algorithms to: compute the word count, produce modified tri-grams around keywords, generate inverted indices for the given dataset and perform relational join on two datasets to ...

This repository contains an implementation of the MapReduce algorithm using the multiprocessing module in Python. The MapReduce algorithm is a programming model for processing and analyzing large ...

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 ...

When the Big Data moniker is applied to a discussion, it’s often assumed that Hadoop is, or should be, involved. But perhaps that’s just doctrinaire. Hadoop, at its core, consists of HDFS (the Hadoop ...

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, ...

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 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 ...