Optimizing Joins in a Map-Reduce for Data Storage and Retrieval Performance Analysis of Query Processing in HDFS for Big Data

Big Data challenges prompting by the fast sending of investigation of information internationally. New excellent presentation systems are presently required to process a regularly expanding volume of information from informational collections. With information immovably close by and with the capacity given by Big Data Technologies to viably store and examine this information, we can discover answers to these inquiries and work to enhance each part of our conduct. The Hadoop structure is an open-source execution of the Map Reduce (MR) figuring model that is picking up force for Big Data investigation in smart matrix applications. For the most part, different DB2 languages turn into a critical application territory for Hadoop by utilizing Query handling. The outcomes demonstrate a hard belief on the measure of reducers and IO execution of the group, which describes the regular assessment that MR is IO-bound. These outcomes can look at the execution conduct of various languages and fill in as a reason for understanding the impact of design parameters on the last execution.