Many of the challenges that have to be faced in Industry 4.0 involve the management and analysis of huge amount of data (e.g. sensor data management and machine-fault prediction in industrial manufacturing, web-logs analysis in e-commerce). To handle the so-called Big Data management and analysis, a plethora of frameworks has been proposed in the last decade. Many of them are focusing on the parallel processing paradigm, such as MapReduce, Apache Hive, Apache Flink. However, in this jungle of frameworks, the performance evaluation of these technologies is not a trivial task, and strictly depends on the application requirements. The scope of this paper is to compare two of the most employed and promising frameworks to manage big data: Apache Flink and Apache Hive, which are general purpose distributed platforms under the umbrella of the Apache Software Foundation. To evaluate these two frameworks we use the benchmark BigBench, developed for Apache Hive. We re-implemented the most significant queries of Apache Hive BigBench to make them work on Apache Flink, in order to be able to compare the results of the same queries executed on both frameworks. Our results show that Apache Flink, if it is configured well, is able to outperform Apache Hive.

BigBench workload executed by using Apache Flink / Bergamaschi, Sonia; Gagliardelli, Luca; Simonini, Giovanni; Zhu, Song. - 11(2017), pp. 695-702. ((Intervento presentato al convegno 27th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM) tenutosi a Modena, ITALY nel JUN 27-30, 2017.

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