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Bigdata Analytics: Comparative Study of Tools

Sri Vasavi College, Erode Self-Finance Wing, 3rd February 2017. National Conference on Computer and Communication, NCCC’17. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

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The term “Big Data” was first introduced to the computing world by Roger Magoulas from O’Reilly media in 2005 in order to define a great amount of data that traditional data management techniques cannot manage and process due to the complexity and size of this data. A study on the Evolution of Big Data as a Research and Scientific Topic shows that the term “Big Data” was present in research starting with 1970s but has been comprised in publications in 2008. Nowadays the Big Data concept is treated from different points of view covering its implications in many fields. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network data. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyze some of the different analytics methods and tools which can be applied to big data, as well as the opportunities provided by the application of big data analytics in various decision domains.


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Big data, Big data analytics, Business analytics, Bid data tools.

  • Format Volume 5, Issue 1, No 2, 2017
  • Copyright All Rights Reserved ©2017
  • Year of Publication 2017
  • Author H. Vignesh Ramamoorthy, G. Murugesan
  • Reference IJCS-164
  • Page No 995-1003

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