Apache Hadoop - A Closer Look
Apache Hadoop offers solutions for the large-scale collection and processing of data. The ASF serves as the central repository and distribution point for the projects, with Apache serving as the core community of users and developers. Apache Hadoop aims to ease the deployment of large applications through its support for the Java and Java platform on the servers. Apache Hadoop was developed by Juhan Lamb Pedrick, Alex Balcov, Michael J. Cafarella, Gerald M. Glassner and Raymond C. Tsouline. It is based on the technology of the NoSQL database management system that was first developed at Facebook and later used by Twitter and Google.
Apache Hadoop includes wide-ranging technology that include Map-Reduce, Yago, Summation, and much more. Apache Hadoop was developing to make the Map-Reduce framework more effective and simpler to use, while providing an intuitive user interface for programmers. Apache Hadoop aims to scale up from traditional server installations to a fully-parallel distributed system, executing thousands of tasks in parallel. It is capable of handling large amounts of data-intensive tasks by allowing each worker to divide work into small pieces so that large tasks can be divided efficiently by the underlying cluster.
Distributed Data Analytics With the help of Apache Hadoop, users can access real-time data from any device, without requiring the need of expensive distributed systems or storage mediums. Apache Hadoop offers highly scalable and highly efficient data-warehousing and data-processing technologies. The Map Reduce framework allows users to effectively reduce the Map complexity, allowing the development of more Complex Graphs and Business Intelligence (BI) applications. Users are also able to build simple Extract-Transform-Load (ETL) applications, which allow ETL handlers to efficiently handle complex transformations.
