Setting Up a Big Data Project : Challenges, Opportunities, Technologies and Optimization
(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
(Less)
- author
- Zicari, Roberto V.
; Rosselli, Marten
; Ivanov, Todor
; Korfiatis, Nikolaos
; Tolle, Karsten
; Niemann, Raik
and Reichenbach, Christoph
LU
- publishing date
- 2016
- 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}}, }