Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It’s designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Here’s a brief overview of how Hadoop works:

1. Hadoop Distributed File System (HDFS): This is the storage part of Hadoop, designed to store large files across multiple machines. It breaks big data into blocks and distributes them across several nodes in a cluster.

2. MapReduce: This is the processing engine in Hadoop. It allows data to be processed parallelly, dramatically reducing the computation time.

3. YARN (Yet Another Resource Negotiator): This manages resources in the clusters and uses them for scheduling users’ applications.

4. Hadoop Common: These are libraries and utilities needed by other Hadoop modules.

5. Other components: Hadoop’s ecosystem includes other projects such as Apache Hive, Apache HBase, Apache Pig, Apache ZooKeeper, and many others that extend the capabilities of Hadoop.

Hadoop is particularly suitable for large-scale data processing applications like data mining, data warehousing, and processing massive datasets for machine learning.