Manuscript Number : CSEIT1726297
Data Analysis Using R and Hadoop
Authors(3) :-Amit Rajbanshi, Birendra Kumar Sah, C. K. Raina Analyzing and managing huge information may be very hard exploitation classical means like electronic data service management systems or desktop package package packages for statistics and image. Instead, huge information desires huge clusters with an entire heap or even thousands of computing nodes. Official statistics is progressively} considering huge information for clarification new statistics as a results of huge information sources would possibly manufacture additional relevant and timely statistics than ancient sources. one of the package package tools successfully and wide unfold used for storage and method of huge information sets on clusters of artefact hardware is Hadoop. Hadoop framework contains libraries, a distributed file-system (HDFS), and a resource-management platform and implements a version of the MapReduce programming model for big scale process. throughout this paper we've got an inclination to analyze the possibilities of integration Hadoop with R that would be a stylish package package used for applied mathematics computing and information image. we've got an inclination to gift three ways in which of integration them: R with Streaming, Rhipe and RHadoop which we have a tendency to emphasize the advantages and downsides of each answer.
Amit Rajbanshi R, Big Data, Hadoop, Rhipe, Rhadoop, Streaming Publication Details Published in : Volume 2 | Issue 6 | November-December 2017 Article Preview
Department of Computer Science and Engineering, Adesh College of Engineering & Technology, Chandigarh, Kharar, Punjab, India
Birendra Kumar Sah
Department of Computer Science and Engineering, Adesh College of Engineering & Technology, Chandigarh, Kharar, Punjab, India
C. K. Raina
Department of Computer Science and Engineering, Adesh College of Engineering & Technology, Chandigarh, Kharar, Punjab, India
Date of Publication : 2017-12-31
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1093-1097
Manuscript Number : CSEIT1726297
Publisher : Technoscience Academy