What Is 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 a simple programming model. 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-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.
The project includes these subprojects:
Hadoop Common: The common utilities that support the other Hadoop subprojects.
Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
Hadoop MapReduce: A software framework for distributed processing of large data sets on compute clusters.
Other Hadoop-related projects at Apache include:
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.
ZooKeeper™: A high-performance coordination service for distributed applications.
Reference : http://hadoop.apache.org/
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 a simple programming model. 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-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.
Hadoop Common: The common utilities that support the other Hadoop subprojects.
Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
Hadoop MapReduce: A software framework for distributed processing of large data sets on compute clusters.
Other Hadoop-related projects at Apache include:
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.
ZooKeeper™: A high-performance coordination service for distributed applications.
Reference : http://hadoop.apache.org/
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