Evaluation Model for Big Data Integration Tools

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 932)


Given the growing demand and need by enterprises for data and information to positively support the decision-making process, there is no doubt about the importance of selecting the correct and appropriate integration tool for the different types of business. For this reason, the essential objective of this study is to create a model that will serve as a basis to evaluate the different alternatives and solutions that exist in the market able to overcome the big data integration challenges. The evaluation process of data integration product begins with the definition and prioritisation of critical requirements and criteria. In this evaluation model, the characteristics evaluated are categorised into three main groups: ease of integration and implementation, quality of service and support, and costs. After identifying the essential criteria and characteristics, it is necessary to determine the weights that these criteria should have in the evaluation. Then, it needs to verify which solutions existing in the market best fit the needs of the business and can satisfy them more effectively. And lastly, compare those solutions adopting this framework. It is essential to carry out a weighted evaluation, based on well-defined criteria like ease of use, quality of technical support, data privacy and security. This process is fundamental to verify if the solution offers what the organization needs if it meets the business requirements and their integration needs.


Big data fabric Evaluation model Big data integration tools 



This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.


  1. 1.
    Zikopoulos, P., Eaton, C.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media, New York (2011)Google Scholar
  2. 2.
    Srivastava, D.: Big data integration. In: Proceedings of the 19th International Conference on Management of Data, p. 3. Computer Society of India, December 2013Google Scholar
  3. 3.
    Baron, B., Musolesi, M.: Interpretable Machine Learning for Privacy-Preserving IoT and Pervasive Systems. arXiv preprint arXiv:1710.08464 (2017)
  4. 4.
    Volpato, T., Rufino, R.R., Dias, J.W.: Big Data – Transformando Dados em Decisões. University of Paranaense, Paranavaí (2014)Google Scholar
  5. 5.
    Cassavia, N., Dicosta, P., Masciari, E., Saccà, D.: Data preparation for tourist data big data warehousing. In: Proceedings of 3rd International Conference on Data Management Technologies and Applications, pp. 419–426. SCITEPRESS-Science and Technology Publications, Lda, August 2014Google Scholar
  6. 6.
    Oussous, A., Benjelloun, F.Z., Lahcen, A.A., Belfkih, S.: Big data technologies: a survey. J. King Saud Univ. Comput. Inf. Sci. 30(4), 431–448 (2017). Scholar
  7. 7.
    Pulse, U.G.: Big data for development: Challenges & opportunities. Naciones Unidas, Nueva York, mayo 2012Google Scholar
  8. 8.
    Gupta, S., Chaudari, M.S.: Big data issues and challenges. Int. J. Recent. Innov. Trends Comput. Commun. 3(2), 062–066 (2015)Google Scholar
  9. 9.
    Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., Khan, S.U.: The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2015)CrossRefGoogle Scholar
  10. 10.
    Lima, C.A.R., Calazans, J.D.H.C.: Pegadas Digitais: “Big Data” E Informação Estratégica Sobre O Consumidor. NT – Sociabilidade, novas tecnologias, consumo e estratégias de mercado do SIMSOCIAL, 2013 (2013)Google Scholar
  11. 11.
    Günther, W.A., Mehrizi, M.H.R., Huysman, M., Feldberg, F.: Debating big data: a literature review on realizing value from big data. J. Strateg. Inf. Syst. 26, 191–209 (2017)CrossRefGoogle Scholar
  12. 12.
    Izzi, M., Warrier, S., Leganza, G., Yuhanna, N.: Big Data Fabric Drives Innovation and Growth. Next-Generation Big Data Management Enables Self-Service and Agility (2016)Google Scholar
  13. 13.
    Hoberman, E., Leganza, G., Yuhanna, N.: The Forrester Wave™: Big Data Fabric, Q2 2018. Tools and Technology: The Data Management Playbook (2018)Google Scholar
  14. 14.
    Beyer, M., Thoo, E., Zaidi, E.: Gartner Magic Quadrant for Data Integration Tools (2018)Google Scholar
  15. 15.
    Talend Data Fabric. A single, unified platform for modern data integration and management. Accessed 21 Oct 2018
  16. 16.
    IBM InfoSphere Information Server. Flexibly meet your unique requirements — from data integration to data quality and data governance. Accessed 1 Nov 2018
  17. 17.
    Informatica Intelligent Data Platform - Powered by CLAIRE. Accessed 2 Nov 2018
  18. 18.
    Lima, L.: Big data for data analysis in financial industry (Master dissertation). University of Minho, Guimarães, Portugal (2014)Google Scholar
  19. 19.
    Marakas, G.M., O’Brien, J.A.: Introduction to Information Systems. McGraw-Hill/Irwin, New York (2013)Google Scholar
  20. 20.
    Lněnička, M.: AHP model for the big data analytics platform selection. Acta Inform. Prag. 4(2), 108–121 (2015)CrossRefGoogle Scholar
  21. 21.
    Altalhi, A.H., Luna, J.M., Vallejo, M.A., Ventura, S.: Evaluation and comparison of open source software suites for data mining and knowledge discovery. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 7(3), e1204 (2017)CrossRefGoogle Scholar
  22. 22.
    Saaty, T.L.: Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1(1), 83–98 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Algoritmi Research CenterUniversity of MinhoGuimarãesPortugal

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