May 26, 2012 \ Ananth TM \ 1 Comment Hadoop! – What is Hadoop? Apache Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance. Rather than relying on high-end hardware, the resiliency of these clusters comes from the software’s ability to detect and handle failures at the application layer.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. http://hadoop.apache.org/ for more details 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. 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. Scalable– New nodes can be added as needed, and added without needing to change data formats, how data is loaded, how jobs are written, or the applications on top. Cost effective– Hadoop brings massively parallel computing to commodity servers. The result is a sizeable decrease in the cost per terabyte of storage, which in turn makes it affordable to model all your data. Flexible– Hadoop is schema-less, and can absorb any type of data, structured or not, from any number of sources. Data from multiple sources can be joined and aggregated in arbitrary ways enabling deeper analyses than any one system can provide. Fault tolerant– When you lose a node, the system redirects work to another location of the data and continues processing without missing a beat. IBM and Hadoop Eighty percent of the world’s data is unstructured, and most businesses don’t even attempt to use this data to their advantage. Imagine if you could afford to keep all the data generated by your business? Imagine if you had a way to analyze that data?IBM InfoSphere BigInsights brings the power of Hadoop to the enterprise. With built-in analytics, extensive integration capabilities and the reliability, security and support that you require, IBM can help put your big data to work for you.Other Hadoop-related projects at Apache include:Hadoop changes the economics and the dynamics of large scale computing. Its impact can be boiled down to four salient characteristics.
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