%0 Book Section %1 HeiligVoss17p29 %A Heilig, Leonard %A Voß, Stefan %B Big Data Management %C Cham %D 2017 %E Garcıa Márquez, Fausto Pedro %E Lev, Benjamin %I Springer %K 01624 springer paper enterprise business economy data pattern recognition analysis software architecture cloud design zzz.big %P 29--45 %R 10.1007/978-3-319-45498-6_2 %T Managing Cloud-Based Big Data Platforms: A Reference Architecture and Cost Perspective %X The development of big data applications is closely linked to the availability of scalable and cost-effective computing capacities for storing and processing data in a distributed and parallel fashion, respectively. Cloud providers already offer a portfolio of various cloud services for supporting big data applications. Large companies like Netflix and Spotify use those cloud services to operate their big data applications. In this chapter, we propose a generic reference architecture for implementing big data applications based on state-of-the-art cloud services. The applicability and implementation of our reference architecture is demonstrated for three leading cloud providers. Given these implementations, we analyze main pricing schemes and cost factors to compare respective cloud services based on a big data streaming use case. Derived findings are essential for cloud-based big data management from a cost perspective. %@ 978-3-319-45497-9

@incollection{HeiligVoss17p29, abstract = {The development of big data applications is closely linked to the availability of scalable and cost-effective computing capacities for storing and processing data in a distributed and parallel fashion, respectively. Cloud providers already offer a portfolio of various cloud services for supporting big data applications. Large companies like Netflix and Spotify use those cloud services to operate their big data applications. In this chapter, we propose a generic reference architecture for implementing big data applications based on state-of-the-art cloud services. The applicability and implementation of our reference architecture is demonstrated for three leading cloud providers. Given these implementations, we analyze main pricing schemes and cost factors to compare respective cloud services based on a big data streaming use case. Derived findings are essential for cloud-based big data management from a cost perspective.}, added-at = {2016-12-31T20:20:35.000+0100}, address = {Cham}, author = {Heilig, Leonard and Voß, Stefan}, biburl = {https://www.bibsonomy.org/bibtex/2366cf128d6893703c884f12287050bec/flint63}, booktitle = {Big Data Management}, crossref = {GarciaMarquezLev2017}, doi = {10.1007/978-3-319-45498-6_2}, editor = {Garc{\i}a M\'{a}rquez, Fausto Pedro and Lev, Benjamin}, file = {SpringerLink:2017/HeiligVoss17p29.pdf:PDF}, groups = {public}, interhash = {bfaa0e33d357e474809f86d7d32702ed}, intrahash = {366cf128d6893703c884f12287050bec}, isbn = {978-3-319-45497-9}, keywords = {01624 springer paper enterprise business economy data pattern recognition analysis software architecture cloud design zzz.big}, pages = {29--45}, publisher = {Springer}, timestamp = {2017-07-13T17:38:43.000+0200}, title = {Managing Cloud-Based Big Data Platforms: A Reference Architecture and Cost Perspective}, username = {flint63}, year = 2017 }