Hadoop is a popular open-source framework for distributed storage and processing of large datasets. It consists of several key components, including HDFS (Hadoop Distributed File System) and MapReduce. Here’s an overview of each component:

1. HDFS (Hadoop Distributed File System):

   • HDFS is the storage component of Hadoop.
   • It is designed to store and manage large datasets across a cluster of commodity hardware.
   • HDFS divides large files into smaller blocks (typically 128MB or 256MB) and replicates these blocks across multiple nodes in a Hadoop cluster for fault tolerance.
   • HDFS follows a master-slave architecture with two key components: the NameNode (master), which manages metadata and namespace, and DataNodes (slaves), which store data blocks.
   • It is optimized for high throughput and is commonly used for storing data that will be processed using Hadoop’s processing frameworks like MapReduce.

2. MapReduce:

   • MapReduce is a programming model and processing framework for distributed data processing.
   • It is a core component of Hadoop and allows developers to write parallel processing jobs for large-scale data processing.
   • MapReduce divides a data processing task into two phases: the Map phase and the Reduce phase.
   • In the Map phase, input data is processed and transformed into key-value pairs by a “mapper” function.
   • In the Reduce phase, the intermediate key-value pairs generated by mappers are grouped by key and processed by a “reducer” function.
   • MapReduce is suitable for batch processing tasks, and it works well with HDFS as it can read data directly from HDFS, process it in parallel, and write results back to HDFS.

Hadoop, HDFS, and MapReduce work together to enable distributed storage and processing of large datasets. HDFS provides a reliable and scalable storage layer, while MapReduce allows for parallel and distributed data processing. Over time, the Hadoop ecosystem has expanded to include various other components and tools for different data processing and analytics needs, making it a comprehensive platform for big data applications.