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Scalable Hadoop-Based Infrastructure for Big Data Analytics

  • Irina Astrova
  • Arne Koschel
  • Felix Heine
  • Ahto Kalja
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 838)

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.

Keywords

Big Data Cloud computing Hadoop 

Notes

Acknowledgement

Irina Astrova’s and Ahto Kalja’s work was supported by the Estonian Ministry of Education and Research institutional research grant IUT33-13.

References

  1. 1.
    White, T.: Hadoop: The Definitive Guide, 3rd edn. O’Reilly Media, Sebastopol (2012)Google Scholar
  2. 2.
    Shook, A.: MapReduce Design Patterns. O’Reilly Media, Sebastopol (2013)Google Scholar
  3. 3.
    Malewicz, G., Matthew, A., Bik, A., Dehnert, J., Horn, I., Leiser, N., Czajkowski, G.: Pregel: a system for large-scale graph processing. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, New York, USA (2010)Google Scholar
  4. 4.
    Koschel, A., Heine, F., Astrova, I., Korte, F., Rossow, T., Stipkovic, S.: Efficiency experiments on Hadoop and Giraph with PageRank. In: Proceedings of 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Heraklion, Crete, Greece, pp. 328–331. IEEE (2016)Google Scholar
  5. 5.
    Havanki, B.: Moving Hadoop to the Cloud Harnessing Cloud Features and Flexibility for Hadoop Clusters. O’Reilly Media, Sebastopol (2017)Google Scholar
  6. 6.
    Astrova, I., Koschel, A., Lennart, M.H., Nahle, H.: Offering Hadoop as a cloud service. In: Proceedings of the 2016 SAI Computing Conference, London, UK, pp. 589–595. IEEE (2016)Google Scholar
  7. 7.
    Kalja, A., Reitsakas, A., Saard, N.: e-Government in Estonia: best practices. In: Anderson, T.R., Daim, T.U., Kocaoglu, D.F., Piscataway, N.J. (eds.) Technology Management: A Unifying Discipline for Melting the Boundaries. pp. 500–506. IEEE (2005)Google Scholar
  8. 8.
    Kalja, A., Robal, T., Vallner, U.: New generations of Estonian e-Government components. In: Proceedings of the 2015 PICMET, Portland, Oregon, USA, pp. 625–631. IEEE (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Irina Astrova
    • 1
  • Arne Koschel
    • 2
  • Felix Heine
    • 2
  • Ahto Kalja
    • 1
  1. 1.Department of Software Science, School of ITTallinn University of TechnologyTallinnEstonia
  2. 2.Faculty IV, Department of Computer ScienceHannover University of Applied Sciences and ArtsHannoverGermany

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