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Wildfire: HTAP for Big Data

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Encyclopedia of Big Data Technologies

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Abstract

Emerging large-scale real-time analytic applications (real-time inventory/pricing/recommendations, fraud detection, risk analysis, IoT, etc.) require data management systems that can handle fast transactions (OLTP) and analytics (OLAP) simultaneously. Some of them even require analytical queries as part of a transaction. Efficient processing of transactional and analytical requests, however, leads to different design decisions in a system. This article presents the Wildfire system, which targets hybrid transactional and analytical processing (HTAP) for big data. Wildfire leverages Apache Spark to enable large-scale data processing with different types of complex analytical requests and columnar data processing to enable fast transactions and analytics concurrently.

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Author information

Authors and Affiliations

  1. IBM Research – Almaden, San Jose, CA, USA

    Ronald Barber, Vijayshankar Raman, Richard Sidle & Yuanyuan Tian

  2. IT University of Copenhagen, Copenhagen, Denmark

    Pınar Tözün

Authors
  1. Ronald Barber
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  2. Vijayshankar Raman
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  3. Richard Sidle
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  4. Yuanyuan Tian
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  5. Pınar Tözün
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Corresponding author

Correspondence to Vijayshankar Raman .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Sherif Sakr

  2. Sch of Info Techno, Building J12, University of Sydney Sch of Info Techno, Building J12, Sydney, Australia

    Albert Zomaya

Section Editor information

  1. IBM Almaden Research Center, SAN JOSE, CA, USA

    Yuanyuan Tian

  2. IBM Research – Almaden, San Jose, CA, USA

    Fatma Özcan

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© 2018 Springer International Publishing AG, part of Springer Nature

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Barber, R., Raman, V., Sidle, R., Tian, Y., Tözün, P. (2018). Wildfire: HTAP for Big Data. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_257-1

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  • DOI: https://doi.org/10.1007/978-3-319-63962-8_257-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63962-8

  • Online ISBN: 978-3-319-63962-8

  • eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering

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