What is Hadoop?

Hadoop is an open-source framework designed for distributed storage and processing of large data sets using a cluster of commodity hardware. It provides a scalable and fault-tolerant solution for handling big data. The core components of Hadoop include:

  1. Hadoop Distributed File System (HDFS): A distributed file system that stores data across multiple machines, providing high throughput access to data.
  2. MapReduce: A programming model and processing engine for distributed computing that processes large data sets in parallel.

Hadoop Versions:

Hadoop has seen several versions, with each release introducing enhancements, bug fixes, and new features. Major Hadoop versions include:

  1. Hadoop 1.x (2006-2012): The initial version featuring HDFS and MapReduce.
  2. Hadoop 2.x (2012-2020): Introduced YARN (Yet Another Resource Negotiator) to manage resources and support for non-MapReduce distributed processing models.
  3. Hadoop 3.x (2017-present): Brought significant improvements, including support for erasure coding in HDFS, enhancements in YARN, and support for more modern hardware and systems.

Hadoop Ecosystem:

The Hadoop ecosystem is a collection of various tools and projects that complement and extend the capabilities of the core Hadoop framework. Some key components include:

Hadoop Ecosystem
  1. Apache Hive: A data warehousing and SQL-like query language for Hadoop.
  2. Apache HBase: A NoSQL, distributed database that provides real-time read and write access to large datasets.
  3. Apache Pig: A high-level platform for creating MapReduce programs used for analyzing large data sets.
  4. Apache Spark: A fast and general-purpose cluster computing system for big data processing.
  5. Apache ZooKeeper: A centralized service for maintaining configuration information, naming, providing distributed synchronization, and group services.
  6. Apache Oozie: A workflow scheduler for managing Hadoop jobs.
  7. Apache Sqoop: A tool for transferring data between Hadoop and relational databases.
  8. Apache Flume: A distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.
  9. Apache Mahout: A machine learning library for scalable data mining and machine learning algorithms.
  10. Apache Ambari: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters.

It’s important to note that various distributions of Hadoop exist, such as Cloudera, Hortonworks (now part of Cloudera), and Apache Hadoop, each with its own set of tools and versioning within the ecosystem. The ecosystem is dynamic, with new projects continuously being added to address evolving big data challenges.

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