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

Roberto V Zicari, Marten Rosselli, Todor Ivanov, Nikolaos Korfiatis, Karsten Tolle, Raik Niemann, Christoph Reichenbach

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

15 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationBig Data Optimization: Recent Developments and Challenges
EditorsAli Emrouznejad
Number of pages31
ISBN (Electronic)978-3-319-30265-2
ISBN (Print)978-3-319-30263-8
Publication statusPublished - 2016

Publication series

NameStudies in Big Data
ISSN (Print)2197-6503

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