Big Data

Big Data

In a distributed environment, data should be meticulously arranged across several computers to avoid inconsistency and redundancy in the results of any given problem. Moreover, data should be processed carefully to achieve low latency. Hence, several factors influence the speed of the operation in a distributed computing system—the way data is stored, the storage algorithm to manage the distributed data, the parallel computing algorithm to process distributed data, and the fault tolerance check on each node. In a nut shell, Apache Hadoop uses a programming model called MapReduce for processing and generating large data sets using parallel computing. The MapReduce programming model was initially introduced by Google to support distributed computing on large data sets in clusters of computers. Inspired by Google’s work, Apache came up with Hadoop, an open source framework for distributed computing. Written in Java, Hadoop is platform independent and easy to install and use in any commodity machine with Java. This eliminates the use of heavier hardware to process big data.