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An experimental survey on big data frameworks

Abstract : Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks with several representative batch and iterative workloads. This survey is concluded with a presentation of best practices related to the use of studied frameworks in several application domains such as machine learning, graph processing and real-world applications.
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Contributor : Sabeur Aridhi <>
Submitted on : Monday, November 19, 2018 - 10:20:18 AM
Last modification on : Wednesday, February 24, 2021 - 4:24:02 PM

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Wissem Inoubli, Sabeur Aridhi, Haithem Mezni, Mondher Maddouri, Engelbert Mephu Nguifo. An experimental survey on big data frameworks. Future Generation Computer Systems, Elsevier, 2018, 86, pp.546 - 564. ⟨10.1016/j.future.2018.04.032⟩. ⟨hal-01926259⟩



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