Data analytics has been rapidly growing in a variety of application areas like mining business intellect for processing the huge amount of data. MapReduce programming paradigm adds itself well to these data-intensive analytics jobs, given its one of the well known ability to scale-out and force several machines to parallely process data. This paper introduces a detailed analysis of big data over cloud computing with several mapred techniques from system and application aspects. Here in this work we say that such Mapper and Reducer based analytics provide a better result over cloud platform. However, End-Users in this environment has the ability to use MapReduce applications to minimize the incurred cost, while obtaining the best performance. From the implementation point of view, we describe the key issues and challenges of big data on cloud and on local system as well. At last, the challenges come across in implementing MapReduce functions over Hadoop and the analysis of standalone, clustered and virtualized systems over our test-bed.

Cloud computing Big data Hadoop HDFS Map Reduce virtualization