Setting Up a Big Data Project: Challenges, Opportunities, Technologies and Optimization

Zicari, Roberto V, Rosselli, Marten, Ivanov, Todor, Korfiatis, Nikolaos ORCID:, Tolle, Karsten, Niemann, Raik and Reichenbach, Christoph (2016) Setting Up a Big Data Project: Challenges, Opportunities, Technologies and Optimization. In: Big Data Optimization: Recent Developments and Challenges. Studies in Big Data, 18 . Springer, pp. 17-47. ISBN 978-3-319-30263-8

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In the first part of this chapter we illustrate how a big data project can be set up and optimized. We explain the general value of big data analytics for the enterprise and how value can be derived by analyzing big data. We go on to introduce the characteristics of big data projects and how such projects can be set up, optimized and managed. Two exemplary real word use cases of big data projects are described at the end of the first part. To be able to choose the optimal big data tools for given requirements, the relevant technologies for handling big data are outlined in the second part of this chapter. This part includes technologies such as NoSQL and NewSQL systems, in-memory databases, analytical platforms and Hadoop based solutions. Finally, the chapter is concluded with an overview over big data benchmarks that allow for performance optimization and evaluation of big data technologies. Especially with the new big data applications, there are requirements that make the platforms more complex and more heterogeneous. The relevant benchmarks designed for big data technologies are categorized in the last part.

Item Type: Book Section
Faculty \ School: Faculty of Social Sciences > Norwich Business School
UEA Research Groups: Faculty of Social Sciences > Research Groups > Innovation, Technology and Operations Management
Faculty of Social Sciences > Research Centres > Centre for Competition Policy
Depositing User: Pure Connector
Date Deposited: 24 Sep 2016 01:01
Last Modified: 20 Apr 2023 01:10
DOI: 10.1007/978-3-319-30265-2_2

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