Proceedings of the VLDB Endowment
Databases And Analytics, Big Data Architecture
This paper presents BigDAWG, a reference implementation of a new architecture for “Big Data” applications. Such applications not only call for large-scale analytics, but also for real-time streaming support, smaller analytics at interactive speeds, data visualization, and cross-storage-system queries. Guided by the principle that “one size does not fit all”, we build on top of a variety of storage engines, each designed for a specialized use case. To illustrate the promise of this approach, we demonstrate its effectiveness on a hospital application using data from an intensive care unit (ICU). This complex application serves the needs of doctors and researchers and provides real-time support for streams of patient data. It showcases novel approaches for querying across multiple storage engines, data visualization, and scalable real-time analytics.
Aaron J. Elmore, Jennie Duggan, Mike Stonebraker, Magdalena Balazinska, Ugur Çetintemel, Vijay Gadepally, J. Heer, Bill Howe, Jeremy Kepner, Tim Kraska, Samuel Madden, David Maier, Timothy G. Mattson, S. Papadopoulos, J. Parkhurst, Nesime Tatbul, Manasi Vartak, Stan Zdonik (2015). A Demonstration of the BigDAWG Polystore System. Proceedings of the VLDB Endowment. 8(12): 1908-1919.