MapReduce is a programming model for large-scale distributed data processing, inspired by the map and reduce functions of functional programming languages such as Lisp, Haskell, and Python. One of the most important features of MapReduce is that it allows us to hide the low-level implementation, such as message passing or synchronization, from users and split a problem into many partitions. This is a great way to make the parallelization of data processing easy, without any need for communication between the partitions.
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The original Google paper MapReduce: Simplified Data Processing on Large Clusters, can be found in the following link: http://research.google.com/archive/mapreduce.html
MapReduce became mainstream because of Apache Hadoop, which is an open source framework that was derived from Google's MapReduce paper. MapReduce allows us to process massive amounts of data in a distributed cluster. In fact, there are many implementations of the MapReduce programming...