EFFICIENT MAP STORING AND REDUCE READING FOR RELATED BIG DATA BATCH TASKSJournal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.5, No. 2)
Publication Date: 2016-02-29
Authors : Ravneet Kaur Sidhu;
Page : 405-411
Keywords : Big Data; MapReduce; Hadoop Distributed File System; Hadoop.;
Big Data has come up with aureate haste and a clef enabler for the social business. Big Data is bringing a positive change in the decision making process of various business organizations. With the several offerings Big Data has come up with several issues and challenges which are related to the Big Data M anagement, Big Data processing and Big Data analysis. In Big Data definition, Big means a dataset which makes data concept to grow so much that it becomes difficult to manage it by using existing data management concepts and tools. To store and process suc h huge data the Hadoop framework uses MapReduce algorithms that work on the files distributed over cluster of computers in HDFS. Map Reduce is playing a very significant role in processing of Big Data. This paper includes a brief about Big Data and its rel ated issues, emphasizes on role of MapReduce in Big Data processing; aim at finding a solution to improve the processing time and proper utilization of resources. MapReduce is elastic scalable, efficient and fault tolerant for analysing a large set of data .
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Last modified: 2016-02-16 23:10:00