Abstract
Cloud architectures are being used increasingly to support Big Data analytics by organizations that make ad hoc or routine use of the cloud in lieu of acquiring their own infrastructure. On the other hand, Hadoop has become the de-facto standard for storing and processing Big Data. It is hard to overstate how many advantages come with moving Hadoop into the cloud. The most important is scalability, meaning that the underlying infrastructure can be expanded or contracted according to the actual demand on resources. This paper presents a scalable Hadoop-based infrastructure for Big Data analytics, one that gets automatically adjusted if more computing power or storage capacity is needed. Adjustments are transparent to the users – the users seem to have nearly unlimited computation and storage resources.
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Acknowledgement
Irina Astrova’s and Ahto Kalja’s work was supported by the Estonian Ministry of Education and Research institutional research grant IUT33-13.
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Astrova, I., Koschel, A., Heine, F., Kalja, A. (2018). Scalable Hadoop-Based Infrastructure for Big Data Analytics. In: Lupeikiene, A., Vasilecas, O., Dzemyda, G. (eds) Databases and Information Systems. DB&IS 2018. Communications in Computer and Information Science, vol 838. Springer, Cham. https://doi.org/10.1007/978-3-319-97571-9_19
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DOI: https://doi.org/10.1007/978-3-319-97571-9_19
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