MapReduce is a programming framework designed for distributed parallel processing of large data sets. It was introduced by Google in 2004 and later popularized by Hadoop. The primary goal of MapReduce is to simplify the management of computationally intensive jobs across large clusters of machines, ensuring that these jobs can be executed in a parallelized, scalable, and fault-tolerant manner.

The MapReduce process consists of two main stages: the Map stage and the Reduce stage. In the Map stage, a user-defined map function processes input data and produces a set of intermediate key-value pairs. The Reduce stage then takes these key-value pairs, groups them by key, and applies a user-defined reduce function to produce a final output. This approach allows for efficient data processing by distributing tasks across multiple workers, enabling parallel execution ²³⁵.

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