Abstract: The rising popularity of large-scale, real-time analytics applications — such as real-time inventory and pricing, mobile applications that give you suggestions, fraud detection, risk analysis, etc. — emphasize the need for scalable data management systems that can handle both fast transactions and analytics concurrently. However, efficient processing of transactional and analytical requests require very different optimizations and architectural decisions in a system.
This talk presents the Wildfire system, which targets Hybrid Transactional and Analytical Processing (HTAP). Wildfire leverages the Spark ecosystem to enable large-scale data processing with different types of complex analytical requests, and columnar data processing to facilitate fast transactions as well as analytics concurrently.
Bio: Dr. Guy M. Lohman recently retired from IBM’s Almaden Research Center in San Jose, California, where he worked for over 34 years as a Distinguished Research Staff Member and Manager.
His group contributed BLU Acceleration to DB2 for Linux, UNIX, and Windows (LUW) 10.5 (2013) and the query engine of the IBM Smart Analytics Optimizer for DB2 for z/OS V1.1 (2010) and the Informix Warehouse Accelerator (2011) products. He was the architect of the Query Optimizer of DB2 LUW and was responsible for its development from 1992 to 1997 (versions 2 – 5), as well as its Visual Explain, efficient sampling, and Index Advisor. Dr. Lohman was elected to the IBM Academy of Technology in 2002, and named an IBM Master Inventor in 2011.
He was the General Co-Chair (with Prof. Sang Cha) of the 2015 IEEE ICDE Conference and General Chair of the 2013 ACM Symposium on Cloud Computing.
He has been awarded 40 U.S. patents and is the (co-)author of over 80 technical papers in the refereed academic literature.