Abstract
Due to the extensive use of SQL, the number of SQL-on-Hadoop systems has significantly increased, transforming Big Data Analytics in a more accessible practice and allowing users to perform ad-hoc querying and interactive analysis. Therefore, it is of upmost importance to understand these querying tools and the specific contexts in which each one of them can be used to accomplish specific analytical needs. Due to the high number of available tools, this work performs a performance evaluation, using the well-known TPC-DS benchmark, of some of the most popular Big Data Analytical tools, analyzing in more detail the behavior of Drill, Hive, HAWQ, Impala, Presto, and Spark.
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Acknowledgements
This work is supported by COMPETE: POCI-01-0145- FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013 and by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project no 002814; Funding Reference: POCI-01-0247-FEDER-002814]. The hardware resources used were provided by INCD – In-fraestrutura Nacional de Computação Distribuída, an unit of FCT.
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Rodrigues, M., Santos, M.Y., Bernardino, J. (2019). Experimental Evaluation of Big Data Analytical Tools. In: Themistocleous, M., Rupino da Cunha, P. (eds) Information Systems. EMCIS 2018. Lecture Notes in Business Information Processing, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-030-11395-7_12
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DOI: https://doi.org/10.1007/978-3-030-11395-7_12
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