Wildfire: HTAP for Big Data

Ronald Barber, Vijayshankar Raman, Richard Sidle, Yuanyuan Tian, Pinar Tözün

Research output: Conference Article in Proceeding or Book/Report chapterEncyclopedia chapterResearchpeer-review


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.
Original languageEnglish
Title of host publicationEncyclopedia of Big Data Technologies
Publication date2019
ISBN (Electronic)978-3-319-63962-8
Publication statusPublished - 2019


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