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Approaches for parallel data loading and data querying

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  • Vlad DIACONITA

    (University of Economic Studies, Bucharest, Romania)

Abstract

This paper aims to bring contributions in data loading and data querying using products from the Apache Hadoop ecosystem. Currently, we talk about Big Data at up to zettabytes scale (1021 bytes). Research in this area is usually interdisciplinary combining elements from statistics, system integration, parallel processing and cloud computing.

Suggested Citation

  • Vlad DIACONITA, 2015. "Approaches for parallel data loading and data querying," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 6(1), pages 78-85, July.
    • Handle: RePEc:aes:dbjour:v:6:y:2015:i:1:p:78-85
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    File URL: http://www.dbjournal.ro/archive/19/19_8.pdf
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    References listed on IDEAS

    as
    1. Lee, In & Lee, Kyoochun, 2015. "The Internet of Things (IoT): Applications, investments, and challenges for enterprises," Business Horizons, Elsevier, vol. 58(4), pages 431-440.
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    More about this item

    Keywords

    Hadoop; loading data; Sqoop; Tez;
    All these keywords.

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