Building a Big Data Platform Using Software without Licence Costs

Author:

Vassilev Vassil,Sowinski-Mydlarz Viktor,Gasiorowski Pawel,Radu Sorin,Nakarmi Sabin,Hristev Martin,Baghaeishiva Reza,Bali Tarun

Abstract

This chapter presents the experience in developing and utilizing Big Data platforms using software without license costs, acquired while working on several projects at two research institutions – the Cyber Security Research Centre of London Metropolitan University in the United Kingdom and the GATE Institute of Sofia University in Bulgaria. Unlike the universal computational infrastructures available from large cloud service providers such as Amazon, Google, Microsoft and others, which provide only a wide range of universal tools, we implemented a more specialized solution for Big Data processing on a private cloud, tailored to the needs of academic institutions, public organizations and smaller enterprises which cannot afford high running costs, or do significant in-house development. Since most of the currently available commercial platforms for Big Data are based on open-source software, such a solution is fully compatible with enterprise solutions from leading vendors like Cloudera, HP, IBM, Oracle and others. Although such an approach may be considered less reliable due to the limited support, it also has many advantages, making it attractive for small institutions with limited budgets, research institutions working on innovative solutions and software houses developing new platforms and applications. It can be implemented entirely on the premises, avoiding cloud service costs and can be tailored to meet the specific needs of the organizations. At the same time, it retains the opportunity for scaling up and migrating the developed solutions as the situations evolve.

Publisher

IntechOpen

Reference28 articles.

1. Gartner, Inc. 10 top strategic technology trends [Internet]. 2023. Available from: [Accessed: July 06, 2023]

2. Moses B, Gavish L. What is a data platform? [Internet]. 2023. Available from: [Accessed: July 07, 2023]

3. Strong A. Containerization vs. virtualization: What is the difference? [Internet]. 2022. Available from: [Accessed: July 07, 2023]

4. Anjomshoaa A et al. Data platforms for data spaces. In: Curry E et al., editors. Data Spaces. Cham: Springer; 2022. DOI: 10.1007/978-3-030-98636-0_3

5. IBM. IBM storage scale Big Data and analytics support [Internet]. 2023. Available from: [Accessed: July 07, 2023]

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