Prompt Title: Hadoop Overview

Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power, and the ability to handle virtually limitless concurrent tasks or jobs.

Hadoop is based on the MapReduce algorithm, which is a computational model that allows for processing and generating large data sets with a parallel, distributed algorithm on a cluster. It consists of two tasks: Map and Reduce. The Map task takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). The Reduce task takes the output from a map as input and combines those data tuples into a smaller set of tuples.

The Hadoop ecosystem includes various components, including:

1. Hadoop Distributed File System (HDFS): A distributed file system that provides high-throughput access to application data.

2. Hadoop YARN: A framework for job scheduling and cluster resource management.

3. Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.

4. Apache Pig and Apache Hive: High-level scripting languages for data exploration, transformation, and querying.

5. Apache HBase: A scalable, distributed database that supports structured data storage for large tables.

6. Apache Spark: An open-source, distributed computing system used for big data processing and analytics.

Hadoop is widely used for big data analytics, data mining, machine learning, and scientific computing. It's designed to scale up from a single server to thousands of machines, each offering local computation and storage.