Various Chunking and Deduduplication Techniques in Big Data

Naresh Kumar¹ , Ishu Devi²

1. CSE Department, UIET-Kurukshetra University, Kurukshetra, India.
2. CSE Department, UIET-Kurukshetra University, Kurukshetra, India.

Correspondence should be addressed to: ishupunia81@gmail.com.

Section:Review Paper, Product Type: Isroset-Journal
Vol.5 , Issue.3 , pp.129-131, Jun-2017

Online published on Jun 30, 2017

Copyright © Naresh Kumar, Ishu Devi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In today’s environment very huge amount of data is generated with duplication. This huge amount of data is called big data. To handle this kind of big data and reduce duplicity from data chunking and deduplication mechanism is used. In deduplication mechanism duplicate data is removed by using chunking and hash functions. In this paper an attempt has been made to converse different chunking and deduplication techniques. A comparative analysis of these techniques with different pros and cons has been presented.

Key-Words / Index Term :
Big Data, Chunking, Deduplication, FBC (Frequency Based Chunking) and CDC (Content Defined Chunking)

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