Hadoop is an open-source framework based on Java that manages the storage and processing of large amounts of data for applications. It uses distributed storage and parallel processing to handle big data and analytics jobs, breaking workloads down into smaller workloads that can be run at the same time. Hadoop consists of four main modules: Hadoop Distributed File System (HDFS), Hadoop Common, MapReduce, and Yet Another Resource Negotiator (YARN) . HDFS is a distributed file system that runs on standard or low-end hardware and provides better data throughput than traditional file systems, in addition to high fault tolerance and native support of large datasets. Hadoop Common provides common Java libraries that can be used across all modules. MapReduce is a programming model and software framework for processing large amounts of data in parallel across a distributed cluster of processors. YARN enables Hadoop users to run applications on processing engines other than MapReduce, such as Spark, Kafka, Apache Flink, and Apache Storm.
Hadoop is used for a variety of purposes, including data storage and archiving, data lakes, marketing analytics, and large-scale enterprise projects that require clusters of servers where specialized data management and programming skills are limited. It is also used for research, production data processing, and analytics that require processing terabytes or petabytes of big data, storing diverse datasets, and data parallel processing. Hadoop is particularly useful for processing large amounts of semi-structured and unstructured data, giving users more flexibility for collecting, managing, and analyzing data than relational databases and data warehouses provide.
In summary, Hadoop is used for managing the storage and processing of large amounts of data for applications, and it provides a cost-effective and scalable solution for big data analytics.
