A Study of Processing Data with MapReduce in Hadoop

4 Pages Posted: 1 Aug 2017

See all articles by Shylesh S

Shylesh S

Srinivas Institute of Management Studies

Date Written: July 30, 2017

Abstract

Hadoop is much more than a highly available, massive data storage engine. One of the main advantages of using Hadoop is that you can combine data storage and processing. Hadoop’s main processing engine is MapReduce, which is currently one of the most popular big-data processing frameworks available. It enables you to seamlessly integrate existing Hadoop data storage into processing, and it provides a unique combination of simplicity and power. Numerous practical problems (ranging from log analysis, to data sorting, to text processing, to pattern-based search, to graph processing, to machine learning, and much more) have been solved using MapReduce. In this paper we will see what is MapReduce and its execution pipeline.

Suggested Citation

S, Shylesh, A Study of Processing Data with MapReduce in Hadoop (July 30, 2017). Available at SSRN: https://ssrn.com/abstract=3011278 or http://dx.doi.org/10.2139/ssrn.3011278

Shylesh S (Contact Author)

Srinivas Institute of Management Studies ( email )

Srinivas Campus, Mangaladevi Road
Pandeshwar
Mangalore, 575001
India

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