HADOOP: A Solution to Big Data Problems using Partitioning Mechanism Map-Reduce IJTSRD


With an increased usage of the internet, the data usage is also getting increased exponentially year on year. So obviously to handle such an enormous data we needed a better platform to process data. So a programming model was introduced called Map Reduce, which process big amounts of data in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner. Since HADOOP has been emerged as a popular tool for BIG DATA implementation, the paper deals with the overall architecture of HADOOP along with the details of its various components.

By Jagjit Kaur | Heena Girdher”HADOOP: A Solution to Big Data Problems using Partitioning Mechanism Map-Reduce”

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018,

URL: http://www.ijtsrd.com/papers/ijtsrd14374.pdf

http://www.ijtsrd.com/computer-science/database/14374/hadoop-a-solution-to-big-data-problems-using-partitioning-mechanism-map-reduce/jagjit-kaur

call for paper Programming Language, international journal Real-time Computing, ugc approved journals Artificial Intelligence




HADOOP: A Solution to Big Data Problems using Partitioning Mechanism Map-Reduce IJTSRD IEEE PAPER





2020 technology trends
2019-TOP-TECHNOLOGIES
2019 papers
2018-TOP-TECHNOLOGIES
2018 papers

IEEE PROJECTS 2019


IEEE PROJECTS CSE 2019
IEEE PROJECTS ECE 2019
IEEE PROJECTS EEE 2019
IEEE PROJECTS VLSI
IEEE PROJECTS EMBEDDED SYSTEM

IEEE PROJECTS


IEEE PROJECTS ECE
IEEE PROJECTS CSE COMPUTER SCIENCE
IEEE PROJECTS ELECTRICAL ENGINEERING
IEEE PROJECTS EEE

IEEE PROJECTS