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Big Data Proprietary Platforms. The Case Of Oracle Exadata

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  • MARIN FOTACHE

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

  • ALEXANDRU TICÄ‚

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

  • IONUÈš HRUBARU

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

  • TEODOR MARIUS SPÃŽNU

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

Abstract

The most prominent Big Data solutions – such as NoSQL systems, Hadoop Frameworks, Spark, etc. – have been open-sourced. Nevertheless, commercial providers have targeted niches of this huge market with products more or less viable and affordable. This paper addresses the problem of benchmarking Big Data platforms with a focus on Oracle Exadata solution provided by one the most important data technologies vendor. Many classical benchmark approaches, such as TPC-H, are based on a predefined set of queries, and consequently they are not prone to predictive modeling. By contrast, for the TPC-H benchmark schema, we generate a set of 500 random queries containing not only tuple filters (WHERE), but also tuple grouping (GROUP BY) and group filters (HAVING), we collected results of the queries execution on four Oracle Exadata settings. Query duration was the outcome variable. Various query parameters, such as the number of joins, the number of attributes of different types within SELECT and WHERE clauses, and also some environment metrics served as predictors. Results were interpreted using exploratory data analysis and also Multivariate Adaptive Regression Splines (MARS) for both predicting the performance and explaining the main drivers of the system performance.

Suggested Citation

  • Marin Fotache & Alexandru Ticä‚ & Ionuèš Hrubaru & Teodor Marius Spãžnu, 2018. "Big Data Proprietary Platforms. The Case Of Oracle Exadata," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 21, pages 45-78, June.
    • Handle: RePEc:aic:revebs:y:2018:j:21:fotachem
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    JEL classification:

    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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