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Big Data Visualization Tools and Techniques

Big Data Visualization Tools and Techniques

Obinna Chimaobi Okechukwu
Copyright: © 2018 |Pages: 32
ISBN13: 9781522531425|ISBN10: 1522531424|EISBN13: 9781522531432
DOI: 10.4018/978-1-5225-3142-5.ch017
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MLA

Okechukwu, Obinna Chimaobi. "Big Data Visualization Tools and Techniques." Handbook of Research on Big Data Storage and Visualization Techniques, edited by Richard S. Segall and Jeffrey S. Cook, IGI Global, 2018, pp. 465-496. https://doi.org/10.4018/978-1-5225-3142-5.ch017

APA

Okechukwu, O. C. (2018). Big Data Visualization Tools and Techniques. In R. Segall & J. Cook (Eds.), Handbook of Research on Big Data Storage and Visualization Techniques (pp. 465-496). IGI Global. https://doi.org/10.4018/978-1-5225-3142-5.ch017

Chicago

Okechukwu, Obinna Chimaobi. "Big Data Visualization Tools and Techniques." In Handbook of Research on Big Data Storage and Visualization Techniques, edited by Richard S. Segall and Jeffrey S. Cook, 465-496. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3142-5.ch017

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Abstract

In this chapter, a discussion is presented on the latest tools and techniques available for Big Data Visualization. These tools, techniques and methods need to be understood appropriately to analyze Big Data. Big Data is a whole new paradigm where huge sets of data are generated and analyzed based on volume, velocity and variety. Conventional data analysis methods are incapable of processing data of this dimension; hence, it is fundamentally important to be familiar with new tools and techniques capable of processing these datasets. This chapter will illustrate tools available for analysts to process and present Big Data sets in ways that can be used to make appropriate decisions. Some of these tools (e.g., Tableau, RapidMiner, R Studio, etc.) have phenomenal capabilities to visualize processed data in ways traditional tools cannot. The chapter will also aim to explain the differences between these tools and their utilities based on scenarios.

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