MapReduce is a programming model and processing technique that is commonly associated with Hadoop and other distributed computing frameworks. It’s designed to process large datasets in parallel across a cluster of computers. Here’s a brief overview of how MapReduce works:

1. Map Phase: In this phase, input data is divided into smaller chunks, and a “mapper” function is applied to each chunk independently. The mapper function takes the input data and produces a set of key-value pairs. These key-value pairs are intermediate results that are grouped by key.

2. Shuffle and Sort: After the map phase, the intermediate key-value pairs are shuffled and sorted based on their keys. This step ensures that all values associated with a particular key are grouped together and ready for the reduce phase.

3. Reduce Phase: In this phase, a “reducer” function is applied to each group of key-value pairs with the same key. The reducer function can aggregate, process, or analyze the data within each group. The output of the reducer phase is typically written to storage or used as the final result.

MapReduce is a powerful paradigm for distributed data processing because it can scale horizontally, allowing for the processing of massive datasets across a cluster of machines. It abstracts away many of the complexities of distributed computing and fault tolerance, making it easier for developers to write parallel processing jobs.