High Performance and Fault Tolerant Distributed File System for Big Data Storage and Processing Using Hadoop

Hadoop is a quickly budding ecosystem of components based on Google's MapReduce algorithm and file system work for implementing MapReduce algorithms in a scalable fashion and distributed on commodity hardware. Hadoop enables users to store and process large volumes of data and analyse it in ways not previously possible with SQL-based approaches or less scalable solutions. Remarkable improvements in conventional compute and storage resources help make Hadoop clusters feasible for most organizations. This paper begins with the discussion of Big Data evolution and the future of Big Data based on Gartner's Hype Cycle. We have explained how Hadoop Distributed File System (HDFS) works and its architecture with suitable illustration. Hadoop's MapReduce paradigm for distributing a task across multiple nodes in Hadoop is discussed with sample data sets. The working of MapReduce and HDFS when they are put all together is discussed. Finally the paper ends with a discussion on Big Data Hadoop sample use cases which shows how enterprises can gain a competitive benefit by being early adopters of big data analytics.