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Big Data Analysis: Big Data Analysis Pipeline and Its Technical Challenges

Big Data Analysis: Big Data Analysis Pipeline and Its Technical Challenges

Rajanala Vijaya Prakash
Copyright: © 2016 |Pages: 11
ISBN13: 9781522501824|ISBN10: 1522501827|EISBN13: 9781522501831
DOI: 10.4018/978-1-5225-0182-4.ch006
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MLA

Prakash, Rajanala Vijaya. "Big Data Analysis: Big Data Analysis Pipeline and Its Technical Challenges." Effective Big Data Management and Opportunities for Implementation, edited by Manoj Kumar Singh and Dileep Kumar G., IGI Global, 2016, pp. 83-93. https://doi.org/10.4018/978-1-5225-0182-4.ch006

APA

Prakash, R. V. (2016). Big Data Analysis: Big Data Analysis Pipeline and Its Technical Challenges. In M. Singh & D. G. (Eds.), Effective Big Data Management and Opportunities for Implementation (pp. 83-93). IGI Global. https://doi.org/10.4018/978-1-5225-0182-4.ch006

Chicago

Prakash, Rajanala Vijaya. "Big Data Analysis: Big Data Analysis Pipeline and Its Technical Challenges." In Effective Big Data Management and Opportunities for Implementation, edited by Manoj Kumar Singh and Dileep Kumar G., 83-93. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0182-4.ch006

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Abstract

The data management industry has matured over the last three decades, primarily based on Relational Data Base Management Systems (RDBMS) technology. The amount of data collected and analyzed in enterprises has increased several folds in volume, variety and velocity of generation and consumption, organizations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. The data-driven world has the potential to improve the efficiencies of enterprises and improve the quality of our lives. There are a number of challenges that must be addressed to allow us to exploit the full potential of Big Data. This article highlights the key technical challenges of Big Data.

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