Open AccessAn Implementation of Map Reduce on the Hadoop for Analyzing Big Data
Author(s) -
Gul Shaira,
Banu Jahangeer,
T.Diliphan Rajkumar
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d1115.1284s219
Subject(s) - computer science , big data , volume (thermodynamics) , map reduce , process (computing) , the internet , computation , distributed computing , architecture , data intensive computing , data processing , programming paradigm , database , data mining , operating system , art , physics , geometry , mathematics , grid computing , algorithm , quantum mechanics , visual arts , programming language , grid
The Speedy development of Internet has led to huge quantities of digital data available online and vast capacity of digital data is increasing and successfully stored. In demand to the process, analyzed, and linked huge volume of stored data to achieve correct Information, some computation is required. Even efficient processing and implementation is needed for scientific data performance analysis. We will compare with already existing MapReduce Technique with Hadoop to afford high performance and efficiency for large volume of dataset. Hadoop distributed architecture with MapReduce programming is analysis here.