Big Data Analytics in Cloud Computing: Effective Deployment of Data Analytics Tools

Big Data Analytics in Cloud Computing: Effective Deployment of Data Analytics Tools

Rajganesh Nagarajan (A. V. C. College of Engineering, India) and Ramkumar Thirunavukarasu (VIT University, India)
Copyright: © 2019 |Pages: 17
DOI: 10.4018/978-1-5225-9023-1.ch018


In this chapter, the authors consider different categories of data, which are processed by the big data analytics tools. The challenges with respect to the big data processing are identified and a solution with the help of cloud computing is highlighted. Since the emergence of cloud computing is highly advocated because of its pay-per-use concept, the data processing tools can be effectively deployed within cloud computing and certainly reduce the investment cost. In addition, this chapter talks about the big data platforms, tools, and applications with data visualization concept. Finally, the applications of data analytics are discussed for future research.
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Categories Of Bigdata And Challenges Associated

With the advent of internet and smart devices, the manipulation of data increases rapidly. In addition, there is no such common mechanism followed for the representation of data. In such scenario, it is important to process different kinds of data (Agresti & Kateri, 2011) before formulating the information. With respect to the existing data processing mechanisms, it is essential to invent a new data processing methodology with less capital investment. Accordingly, cloud computing has been highly recommended to incorporate the data processing activities by running the new data analytics tools. Hence, this section highlights the various categories of big data and its processing tool. Finally, the incorporation of cloud computing enhances the data analytics process in an effective manner.

Categories of Data

  • 1.

    Structured Data: Refers to any kind of data that has a proper format and resides in a record or file. Structured data (Chang et al., 2008) are easy to input, query, store, and analyse. Examples of structured data include numbers, words, and dates.

  • 2.

    Semi-Structured Data: The data that are not following the conventional or relational data base system are called as semi-structured data (Sagiroglu & Sinanc, 2013). The data are not organized in table format. In order to analyse the semi-structured data, the complex rules must be used.

  • 3.

    Unstructured Data: The text messages, location information, videos, and social media information (Feldman & Sanger, 2007) are data that do not follow any prescribed format. Always the size of this data is increasing because of the use of new technological devices such as smartphones. Therefore, the understanding of such data become a more challenging one.

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