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

Zicari, Roberto V. ; Rosselli, Marten ; Ivanov, Todor ; Korfiatis, Nikolaos ; Tolle, Karsten ; Niemann, Raik and Reichenbach, Christoph LU orcid (2016) In Studies in Big Data 18. p.17-47
Abstract

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... (More)

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

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Please use this url to cite or link to this publication:
author
; ; ; ; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Big Data Optimization : Recent Developments and Challenges - Recent Developments and Challenges
series title
Studies in Big Data
editor
Emrouznejad, Ali
volume
18
pages
31 pages
publisher
Springer
external identifiers
  • scopus:85125454798
ISSN
2197-6511
2197-6503
ISBN
978-3-319-30263-8
978-3-319-30265-2
DOI
10.1007/978-3-319-30265-2_2
language
English
LU publication?
no
additional info
Publisher Copyright: © Springer International Publishing Switzerland 2016.
id
1e0ad9e0-4195-4838-8130-513322921457
date added to LUP
2022-04-12 09:43:46
date last changed
2024-06-17 02:49:59
@inbook{1e0ad9e0-4195-4838-8130-513322921457,
  abstract     = {{<p>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</p>}},
  author       = {{Zicari, Roberto V. and Rosselli, Marten and Ivanov, Todor and Korfiatis, Nikolaos and Tolle, Karsten and Niemann, Raik and Reichenbach, Christoph}},
  booktitle    = {{Big Data Optimization : Recent Developments and Challenges}},
  editor       = {{Emrouznejad, Ali}},
  isbn         = {{978-3-319-30263-8}},
  issn         = {{2197-6511}},
  language     = {{eng}},
  pages        = {{17--47}},
  publisher    = {{Springer}},
  series       = {{Studies in Big Data}},
  title        = {{Setting Up a Big Data Project : Challenges, Opportunities, Technologies and Optimization}},
  url          = {{http://dx.doi.org/10.1007/978-3-319-30265-2_2}},
  doi          = {{10.1007/978-3-319-30265-2_2}},
  volume       = {{18}},
  year         = {{2016}},
}