Wildfire: HTAP for Big Data

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

doi:10.1007/978-3-319-63962-8257-1

Wildfire: HTAP for Big Data

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

Data-intensive Systems and Applications

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Encyclopædiartikel › Forskning › peer review

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.

Originalsprog	Engelsk
Titel	Encyclopedia of Big Data Technologies
Forlag	Springer
Publikationsdato	2019
ISBN (Elektronisk)	978-3-319-63962-8
DOI	https://doi.org/10.1007/978-3-319-63962-8257-1
Status	Udgivet - 2019

Emneord

Hybrid Transactional and Analytical Processing (HTAP)
Big Data
Real-Time Analytics
Apache Spark
Columnar Data Processing

Adgang til dokumentet

10.1007/978-3-319-63962-8257-1

https://doi.org/10.1007/978-3-319-63962-8257-1

Citationsformater

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AU - Tian, Yuanyuan

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N1 - Note til JCG: ingen BFI-points for denne publikationstype. /PFOR 13-12-2019 JCG: husk at slette denne note.

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AB - 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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KW - Real-Time Analytics

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Wildfire: HTAP for Big Data

Abstract

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Citationsformater