Big Data Storage Analytics

Big Data Storage Analytics

  IJCTT-book-cover
 		 International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-51 Number-2
Year of Publication : 2017
Authors : Subash Thota
DOI :  10.14445/22312803/IJCTT-V51P111

MLA

Subash Thota "Big Data Storage Analytics". International Journal of Computer Trends and Technology (IJCTT) V51(2):68-74, September 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The amount of data being generated that enterprises acquire every day is increasing exponentially. The size of the databases has been growing at exponential rates in today’s businesses. It is viable to store massive amounts of information on low-cost platforms such as Hadoop. Ironically storage industry has come a long way from providing just a storage system to customers based on customer’s request to storage vendors anticipating customer needs and providing timely storage provisioning and sizing advice for business continuity. One of the significant challenges that the storage industry faces is how to process and analyze these large volumes of data and aligning their products and services according to the capacity and performance requirements of customers. This whitepaper talks about how storage industry can leverage the Big Data ecosystem and data analytics to address the very problem of proactively identifying customer needs.

References
1) Big Data Analytics with R and Hadoop Vignesh Prajapati, Packt Publishing, 1st edition,2013.
2) Thota, S., 2017. Big Data Quality. Encyclopedia of Big Data, pp.1-5.
https://link.springer.com/referenceworkentry/10.1007/978- 3-319-32001-4_240-1
3) MachineLearningwithRBrettLantz,PacktPublishing,1stediti on,October2013.
4) Hadoop For Dummies Dirk deRoos, Paul C. Zikopoulos, Bruce Brown, Rafael Coss, and Roman B. Melnyk, John Wiley & Sons, Inc., 1st edition 2014.
5) Hadoop Beginner`s Guide Garry Turkington, Packt Publishing, 2013.

Keywords
Big Data Analytics, Social Analytics, Storage Analytics, Data Management, Information Quality, Data Mitigation, Metadata, Data Profiling.