Map-Reduce

Map-Reduce is a programming model used for processing large data sets. It works in two main steps: "Map" and "Reduce." In the Map phase, the data is broken down into smaller pieces, and each piece is processed to extract relevant information, like organizing items into categories. In the Reduce phase, the processed data is combined and summarized, such as counting how many items belong to each category. This approach allows for efficient handling of vast amounts of data across many computers, making it a powerful tool for tasks like searching, data analysis, and machine learning.