Welcome to Apache™ Hadoop®!

The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

The project includes these modules:

Hadoop Common: The common utilities that support the other Hadoop modules.
Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
Hadoop YARN: A framework for job scheduling and cluster resource management.
Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.

Other Hadoop-related projects at Apache include:

Ambari™: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which includes support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heatmaps and ability to view MapReduce, Pig and Hive applications visually alongwith features to diagnose their performance characteristics in a user-friendly manner.
Avro™: A data serialization system.
Cassandra™: A scalable multi-master database with no single points of failure.
Chukwa™: A data collection system for managing large distributed systems.
HBase™: A scalable, distributed database that supports structured data storage for large tables.
Hive™: A data warehouse infrastructure that provides data summarization and ad hoc querying.
Mahout™: A Scalable machine learning and data mining library.
Pig™: A high-level data-flow language and execution framework for parallel computation.
Spark™: A fast and general compute engine for Hadoop data. Spark provides a simple and expressive programming model that supports a wide range of applications, including ETL, machine learning, stream processing, and graph computation.
Tez™: A generalized data-flow programming framework, built on Hadoop YARN, which provides a powerful and flexible engine to execute an arbitrary DAG of tasks to process data for both batch and interactive use-cases. Tez is being adopted by Hive™, Pig™ and other frameworks in the Hadoop ecosystem, and also by other commercial software (e.g. ETL tools), to replace Hadoop™ MapReduce as the underlying execution engine.
ZooKeeper™: A high-performance coordination service for distributed applications.

To get started, begin here:

Learn about Hadoop by reading the documentation.
Download Hadoop from the release page.
Discuss Hadoop on the mailing list.

Please head to the releases page to download a release of Apache Hadoop.

A wide variety of companies and organizations use Hadoop for both research and production. Users are encouraged to add themselves to the Hadoop PoweredBy wiki page.

03 September, 2016: Release 3.0.0-alpha1 available

This is the first alpha in a series of planned alphas and betas leading up to a 3.0.0 GA release. The intention is to "release early, release often" to quickly iterate on feedback collected from downstream users.

Please note that alpha releases come with no guarantees of quality or API stability, and are not intended for production use.

Users are encouraged to read the overview of major changes coming in 3.0.0. The full set of release notes and changelog detail all the changes since the previous minor release 2.7.0.

25 August, 2016: Release 2.7.3 available

A point release for the 2.7 line.

Please see the Hadoop 2.7.3 Release Notes for the list of 221 bug fixes and patches since the previous release 2.7.2.

11 February, 2016: Release 2.6.4 available

A point release for the 2.6 line.

Please see the Hadoop 2.6.4 Release Notes for the list of 46 critical bug fixes and since the previous release 2.6.3.

25 January, 2016: Release 2.7.2 (stable) available

A point release for the 2.7 line.

Please see the Hadoop 2.7.2 Release Notes for the list of 155 bug fixes and patches since the previous release 2.7.1.

17 December, 2015: Release 2.6.3 available

A point release for the 2.6 line.

Please see the Hadoop 2.6.3 Release Notes for the list of 35 critical bug fixes and since the previous release 2.6.2.

28 October, 2015: Release 2.6.2 available

A point release for the 2.6 line.

Please see the Hadoop 2.6.2 Release Notes for the list of 15 critical bug fixes and since the previous release 2.6.1.

23 September, 2015: Release 2.6.1 available

A point release for the 2.6 line.

Please see the Hadoop 2.6.1 Release Notes for the list of 158 critical bug fixes and since the previous release 2.6.0.

06 July, 2015: Release 2.7.1 (stable) available

A point release for the 2.7 line. This release is now considered stable.

Please see the Hadoop 2.7.1 Release Notes for the list of 131 bug fixes and patches since the previous release 2.7.0. Please look at the 2.7.0 section below for the list of enhancements enabled by this first stable release of 2.7.x.