• Antony prakash
• Dr.A.Aloysius

In the Big Data society, MapReduce has been viewed as one of the key empowering approaches for taking care of persistently expanding requests on processing assets forced by enormous informational indexes. The purpose behind this is the high adaptability of the MapReduce worldview which considers greatly parallel and circulated execution over a substantial number of processing hubs. This paper recognizes MapReduce issues and difficulties in taking care of Big Data with the target of giving a diagram of the field, encouraging better arranging and administration of Big Data extends, and distinguishing open doors for future research in this field. The recognized difficulties are assembled into four primary classes comparing to Big Data undertakings composes: information stockpiling (social databases and NoSQL stores), Big Data examination (machine learning and intelligent investigation), and web based preparing, and security and protection. In addition, current endeavors went for enhancing and stretching out MapReduce to address recognized difficulties are exhibited. Therefore, by recognizing issues and difficulties MapReduce faces when taking care of Big Data, this examination energizes future Big Data research.

Big Data, Mapping, Reducing, HDFS, Hadoop

"Concept of MapReduce Scheduling and Processing Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 10, page no.282-289, October-2018, Available :http://www.jetir.org/papers/JETIR1810824.pdf

"Concept of MapReduce Scheduling and Processing Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 10, page no. pp282-289, October-2018, Available at : http://www.jetir.org/papers/JETIR1810824.pdf