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Performance Analysis of Two Big Data Technologies on a Cloud Distributed Architecture. Results for Non-Aggregate Queries on Medium-Sized Data

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  • Fotache Marin
  • Hrubaru Ionuț

    (Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iaşi, Romania)

Abstract

Big Data systems manage and process huge volumes of data constantly generated by various technologies in a myriad of formats. Big Data advocates (and preachers) have claimed that, relative to classical, relational/SQL Data Base Management Systems, Big Data technologies such as NoSQL, Hadoop and in-memory data stores perform better. This paper compares data processing performance of two systems belonging to SQL (PostgreSQL/Postgres XL) and Big Data (Hadoop/Hive) camps on a distributed five-node cluster deployed in cloud. Unlike benchmarks in use (YCSB, TPC), a series of R modules were devised for generating random non-aggregate queries on different subschema (with increasing data size) of TPC-H database. Overall performance of the two systems was compared. Subsequently a number of models were developed for relating performance on the system and also on various query parameters such as the number of attributes in SELECT and WHERE clause, number of joins, number of processing rows etc.

Suggested Citation

  • Fotache Marin & Hrubaru Ionuț, 2016. "Performance Analysis of Two Big Data Technologies on a Cloud Distributed Architecture. Results for Non-Aggregate Queries on Medium-Sized Data," Scientific Annals of Economics and Business, Sciendo, vol. 63(s1), pages 21-50, December.
  • Handle: RePEc:vrs:aicuec:v:63:y:2016:i:s1:p:21-50:n:2
    DOI: 10.1515/saeb-2016-0134
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    References listed on IDEAS

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    1. Fox, John, 2003. "Effect Displays in R for Generalised Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i15).
    2. Martin Kowalczyk & Peter Buxmann, 2014. "Big Data and Information Processing in Organizational Decision Processes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 267-278, October.
    3. Ion LUNGU & Bogdan George TUDORICA, 2013. "The Development of a Benchmark Tool for NoSQL Databases," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 4(2), pages 13-20, July.
    4. Kowalczyk, Martin & Buxmann, Peter, 2014. "Big Data and Information Processing in Organizational Decision Processes: A Multiple Case Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65730, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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