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Hadoop and Risk Analytics

Pankesh Bamotra, Saira Banu J. Published in Distributed Processing

International Journal of Applied Information Systems
Year of Publication: 2013
© 2012 by IJAIS Journal
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  1. Pankesh Bamotra and Saira Banu J.. Article: Hadoop and Risk Analytics. International Journal of Applied Information Systems 5(7):38-40, May 2013. BibTeX

    	author = "Pankesh Bamotra and Saira Banu J.",
    	title = "Article: Hadoop and Risk Analytics",
    	journal = "International Journal of Applied Information Systems",
    	year = 2013,
    	volume = 5,
    	number = 7,
    	pages = "38-40",
    	month = "May",
    	note = "Published by Foundation of Computer Science, New York, USA"


This paper brings out the specific use case of Hadoop in risk analytics which forms an important part of every organization. Risk analytics is necessary because from business perspective, business leaders in any organization run into one or other kind of risk. They need to add value proposition to any kind of risk they take on behalf of the organization. But how to assess that risk is a big challenge in today's world. This is because of volume, veracity, velocity and variety of data that need to be analyzed is growing on an ever-increasing scale. This ultimately leads to vulnerability. Every day several petabytes of information is being stored, logged and analyzed, thus putting a bottleneck in the use of traditional RDBMS for real-time analytics. Here is when Hadoop comes as a savior. The paper talks about what Hadoop is, its programming paradigm called MapReduce, how is Hadoop different from traditional RDBMS, the technologies built on top of Hadoop, when to choose and not to choose Hadoop, its limitations and future scope. The use case of Hadoop with respect to risk analytics and that too particular to e-payments industry is also discussed.


  1. Google's MapReduce Programming Model—Revisited, Ralf Lämmel, Science of Computer Programming, Volume 68 Issue 3, October, 2007
  2. Hadoop Operations, Eric Sammer, O'Reilly Publications
  3. Key-Value stores: a practical overview, Computer Science and Media, Ultra-Large-Sites SS09, Stuttgart, Germany
  4. Understanding Risk Management in Emerging Retail Payments, Michele Braun, James McAndrews, William Roberds, and Richard Sullivan, Economic Policy Review, Vol. 14, No. 2, September 2008
  5. A Survey of Extract–Transform–Load Technology. Panos Vassiliadis, International Journal of Data Warehousing and Mining (IJDWM), volume 5, no. 3, pp. 1-27, June 2009, IGI
  6. Born To Be Parallel - Why Parallel Origins Give Teradata Database an Enduring Performance Edge Carrie Ballinger, Teradata White Papers
  7. In-Memory Big Data Analysis with Oracle Exalytics, Mark Rittman, Oracle Openworld 2012, San Francisco, October 2012
  8. IBM Netezza Analytics, IBM Data Sheet, April 2012
  9. Plenary talk: Big data and real time analytics, Rao, G. V, N Appa, Recent Trends in Information Technology (ICRTIT), 2011 International IEEE Conference
  10. Fundamentals Of Database Systems, 5/E, R Elmasri
  11. MapReduce and the Data Scientist, Colin White, BI Research, January 2012
  12. Bigtable: A Distributed Storage System for Structured Data, Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, Robert E. Gruber, Proceedings of OSDI 2006
  13. O'Reilly Media Inc. 's Strata Conference 2013, Santa Clara


Big Data, Hadoop, Risk analysis, RDBMS