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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 distributedprocessing 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.
Welcome to Apache™ Hadoop®!
Page 3 Copyright © 2014 The Apache Software Foundation. All rights reserved.
• 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.
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原文地址：http://www.cnblogs.com/xgqfrms/p/5014053.html
