An Implementation of Map Reduce on the Hadoop for Analyzing Big Data
Gul Shaira Banu Jahangeer1, T. Diliphan Raj Kumar2
1Gul Shaira Banu Jahangeer, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
2Dr. T. Diliphan Raj Kumar, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
Manuscript received on 02 December 2019 | Revised Manuscript received on 20 December 2019 | Manuscript Published on 31 December 2019 | PP: 712-716 | Volume-8 Issue-4S2 December 2019 | Retrieval Number: D11151284S219/2019©BEIESP | DOI: 10.35940/ijrte.D1115.1284S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Map Reduce, Hadoop, Distributed Computing.
Scope of the Article: Big Data Analytics and Business Intelligence