Building a big data platform using software without licence costs

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...

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Main Authors: Vassilev, Vassil, Sowinski-Mydlarz, Viktor, Gasiorowski, Pawel, Radu, Sorin, Nakarmi, Sabin, Hristev, Martin, Baghaeishiva, Reza, Bali, Tarun
Other Authors: Castro, Laura
Format: Book Section
Language:English
Published: IntechOpen 2024
Subjects:
Online Access:https://repository.londonmet.ac.uk/8935/1/Final%20CHAPTER%20VERSION.pdf
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author Vassilev, Vassil
Sowinski-Mydlarz, Viktor
Gasiorowski, Pawel
Radu, Sorin
Nakarmi, Sabin
Hristev, Martin
Baghaeishiva, Reza
Bali, Tarun
author2 Castro, Laura
author_facet Castro, Laura
Vassilev, Vassil
Sowinski-Mydlarz, Viktor
Gasiorowski, Pawel
Radu, Sorin
Nakarmi, Sabin
Hristev, Martin
Baghaeishiva, Reza
Bali, Tarun
author_sort Vassilev, Vassil
collection LMU
description 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.
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spelling oai:repository.londonmet.ac.uk:89352024-05-30T09:39:43Z http://repository.londonmet.ac.uk/8935/ Building a big data platform using software without licence costs Vassilev, Vassil Sowinski-Mydlarz, Viktor Gasiorowski, Pawel Radu, Sorin Nakarmi, Sabin Hristev, Martin Baghaeishiva, Reza Bali, Tarun 000 Computer science, information & general works 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. IntechOpen Castro, Laura 2024-05-29 Book Section PeerReviewed text en cc_by https://repository.londonmet.ac.uk/8935/1/Final%20CHAPTER%20VERSION.pdf Vassilev, Vassil, Sowinski-Mydlarz, Viktor, Gasiorowski, Pawel, Radu, Sorin, Nakarmi, Sabin, Hristev, Martin, Baghaeishiva, Reza and Bali, Tarun (2024) Building a big data platform using software without licence costs. In: Open-source horizons: challenges and opportunities for collaboration and innovation. IntechOpen, London, pp. 1-24. ISBN 9780854661121, 9780854661145 (PDF), 9780854661138 (Print) https://www.intechopen.com/online-first/1155033 10.5772/intechopen.1003268
spellingShingle 000 Computer science, information & general works
Vassilev, Vassil
Sowinski-Mydlarz, Viktor
Gasiorowski, Pawel
Radu, Sorin
Nakarmi, Sabin
Hristev, Martin
Baghaeishiva, Reza
Bali, Tarun
Building a big data platform using software without licence costs
title Building a big data platform using software without licence costs
title_full Building a big data platform using software without licence costs
title_fullStr Building a big data platform using software without licence costs
title_full_unstemmed Building a big data platform using software without licence costs
title_short Building a big data platform using software without licence costs
title_sort building a big data platform using software without licence costs
topic 000 Computer science, information & general works
url https://repository.londonmet.ac.uk/8935/1/Final%20CHAPTER%20VERSION.pdf
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