Various approches to improve MapReduce performance in Hadoop

Every day, Petabytes of data are generated from various business communities across the globe. Generating meaningful insights from this large dataset is a challenging issue. BigData is a combination of homogeneous and heterogeneous data and it can be structured, un-structured or semi-structured. Hadoop is a framework for processing BigData in a distributed way. MapReduce is an aggregation technique used by Hadoop for processing this BigData. Mainly Map and Reduce are the two stages performing in MapReduce approach. This paper focuses different MapReduce scheduling techniques and performance improvement methods in conjunction with Hadoop MapReduce. This paper also focuses the challenges of various MapReduce approaches in BigData analytics.