MapReduce has become a very important distributed process model for large scale data-intensive applications like Web data and data mining. Hadoop is an open source implementation of MapReduce is wide used for large data processing which requires low time response. This paper, address the matter of approach to place data across nodes in an exceedingly way that every node contains a balanced processing load. Given a data intensive application running on a Hadoop MapReduce cluster, the authors’ data placement theme adaptively balances the number of knowledge hold on in every node to realize improved data-processing performance.