Implementation of Big-Data Applications by Sparse Coding Models using Map Reduce Framework

Clustering As a result of the rapid development in cloud computing, it & fundamental to investigate the performance of extraordinary Hadoop MapReduce purposes and to realize the performance bottleneck in a cloud cluster that contributes to higher or diminish performance. It is usually primary to research the underlying hardware in cloud cluster servers to permit the optimization of program and hardware to achieve the highest performance feasible. Hadoop is founded on MapReduce, which is among the most popular programming items for huge knowledge analysis in a parallel computing environment. In this paper, we reward a particular efficiency analysis, characterization, and evaluation of Hadoop MapReduce Word Count Utility. The main aim of this paper is to give implements of Hadoop map-reduce programming by giving a hands-on experience in developing Hadoop based Word-Count and Apriori application. Word count problem using Hadoop Map Reduce framework. The Apriori Algorithm has been used for finding frequent item set using Map Reduce framework.