A Survey Work on Optimization Techniques Utilizing Map Reduce Framework in Hadoop Cluster

Data is one of the most important and vital aspect of different activities in today's world. Therefore vast amount of data is generated in each and every second. A rapid growth of data in recent time in different domains required an intelligent data analysis tool that would be helpful to satisfy the need to analysis a huge amount of data. Map Reduce framework is basically designed to process large amount of data and to support effective decision making. It consists of two important tasks named as map and reduce. Optimization is the act of achieving the best possible result under given circumstances. The goal of the map reduce optimization is to minimize the execution time and to maximize the performance of the system. This survey paper discusses a comparison between different optimization techniques used in Map Reduce framework and in big data analytics. Various sources of big data generation have been summarized based on various applications of big data.The wide range of application domains for big data analytics is because of its adaptable characteristics like volume, velocity, variety, veracity and value .The mentioned characteristics of big data are because of inclusion of structured, semi structured, unstructured data for which new set of tools like NOSQL, MAPREDUCE, HADOOP etc are required. The presented survey though provides an insight towards the fundamentals of big data analytics but aims towards an analysis of various optimization techniques used in map reduce framework and big data analytics.