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 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 ...

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 ...

Finding frequent itemsets is one of the most important fields of data mining. Apriori algorithm is the most established algorithm for finding frequent itemsets from a transactional dataset; however, ...

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, ...

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 ...

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 ...

Abstract: In recent years the MapReduce framework has become one of the most popular parallel computing platform for processing big data. It is frequently used by companies such as Facebook, IBM, and ...