MapReduce is a programming model and processing technique for distributed computing, developed by Google. It enables the processing of large datasets across a cluster of computers in a parallel, reliable, and fault-tolerant manner. The model divides the task into two main functions: the Map function, which processes and filters data to produce key-value pairs, and the Reduce function, which aggregates and summarizes these pairs to produce the final output.

The Map function takes input data and transforms it into a set of intermediate key-value pairs. Each pair consists of a key and a corresponding value, which are then shuffled and sorted by the system. This sorting and shuffling step is crucial as it groups all the values associated with the same key together, making it easier for the Reduce function to process them. This division allows for parallel processing, as different keys can be processed simultaneously on different nodes in the cluster.

The Reduce function takes the grouped key-value pairs and performs an aggregation operation, such as summing, averaging, or concatenation, depending on the specific use case. The output of the Reduce function is then written to a distributed storage system, completing the processing cycle. MapReduce's ability to scale horizontally by adding more nodes to the cluster makes it highly efficient for handling massive datasets, making it a foundational technology for many big data processing frameworks like Hadoop.