Breaking a big data collection into pieces, applying a large number of simultaneous processors to search the pieces, then combining the results is known as MapReduce.
In the field of software engineering, MapReduce can be described as a processing technique through which applications can be written in order for processing large amounts of data.
The two tasks of MapReduce are Map and Reduce. In the Map task, a set of big data is broken down into elements known as tuples. Filtering and sorting of data are done in the Map task. In the Reduce task, the tuples are combined in order to form a shorter version. The Reduce task is like a summary operation. The tuples are searched through a large number of processors.
The input data required for the MapReduce is in the form of a Hadoop file system (HDFS).
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