Addressing big data problem using Hadoop and Map Reduce

The size of the databases used in today´s enterprises has been growing at exponential rates day by day. Simultaneously, the need to process and analyze the large volumes of data for business decision making has also increased. In several business and scientific applications, there is a need to process terabytes of data in efficient manner on daily bases. This has contributed to the big data problem faced by the industry due to the inability of conventional database systems and software tools to manage or process the big data sets within tolerable time limits. Processing of data can include various operations depending on usage like culling, tagging, highlighting, indexing, searching, faceting, etc operations. It is not possible for single or few machines to store or process this huge amount of data in a finite time period. This paper reports the experimental work on big data problem and its optimal solution using Hadoop cluster, Hadoop Distributed File System (HDFS) for storage and using parallel processing to process large data sets using Map Reduce programming framework. We have done prototype implementation of Hadoop cluster, HDFS storage and Map Reduce framework for processing large data sets by considering prototype of big data application scenarios. The results obtained from various experiments indicate favorable results of above approach to address big data problem.