Abstract

As there is an increasing trend of applications being expected to deal with big data that usually do not fit in the main memory of a single machine, analyzing big data is a challenging problem today. For such data-intensive applications, the MapReduce framework has recently attracted considerable attention and started to be investigated as a cost effective option to implement scalable parallel algorithms for big data analysis which can handle petabytes of data for millions of users. MapReduce is a programming model that allows easy development of scalable parallel applications to process big data on large clusters of commodity machines. Google’s MapReduce or its open-source equivalent Hadoop is a powerful tool for building such applications.

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