• Suyash Mishra
• Dr Suryakant yadav

MapReduce is a programming model, proposed by Google initially to process large datasets. It consist of two components Map and Reduce motivated from functional languages e.g. Lisp and Haskell. To achieve desirable result programs task is to define and implement Map and Reduce functions. Map and Reduce should describe the logic encapsulated in both components to achieve desirable result. There are various big data based operations e.g. culling, highlighting, indexing, searching, faceting. It impossible for a single node machine to manage and process such a huge data. Map Reduce regarded as an important function to tackle continuous increasing demands of computing resources required for processing big data sets. MapReduce highly scalability nature permits parallel and distributed processing on multiple computing nodes. This paper talks about MapReduce usages in cloud computing and motivate its use for big data processing into cloud heterogeneous environment

Big data, Iot, Cloud computing, Grid, MapReduce.

"MapReduce processing challenges in cloud environment", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 8, page no.749-753, August-2020, Available :http://www.jetir.org/papers/JETIR2008096.pdf

"MapReduce processing challenges in cloud environment", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 8, page no. pp749-753, August-2020, Available at : http://www.jetir.org/papers/JETIR2008096.pdf