MapReduce is a programming model used to map input data into smaller memory blocks and reduce those blocks into digestible insights. A MapReduce job involves map and reduce tasks and algorithms. Hadoop MapReduce framework maintains the same format of a set of key value pairs for both inputs and outputs. The MapReduce program model accesses big data and filters the massive amounts of data collected through data processing mappers and reducers.
MapReduce programs are often used to migrate immense databases into more scalable, searchable, and ultimately usable formats. The map function takes a large input file, processes that data, and splits it into smaller key value pairs. Then, the map output goes to the reducers to be assigned value for analysis. As corporations now have the technology available to receive tremendous amounts of data, MapReduce can help to organize, analyze, migrate, and better interpret all of this data.
Employing MapReduce functions can reduce the amount of untapped data by pre-sorting and processing information into formats suitable for analytics. Map and reduce output values can provide clearer, actionable insights into customer behavior.