Knowledge Graphs now power many applications across diverse industries such as FinTech, Pharma and Manufacturing. Data volumes are growing at a staggering rate, and graphs with hundreds of billions edges are not uncommon. Computations on such data sets include querying, analytics, pattern mining, and learning. In many use cases, it is necessary to combine these operations seamlessly to extract actionable intelligence as quickly as possible. Katana Graph is a start-up based in Austin and the Bay Area that is building a scale-out platform for seamless, high-performance computing on such graph data sets. We describe the key features of the Katana Graph Engine that enable high performance, and some important use cases for this technology from Katana's customers.
Bio: Keshav Pingali is the CEO of Katana Graph, a start-up in the area of graph computing backed by Intel Capital, Dell Technologies Capital, Redline Capital and Walden International, and a professor in the Department of Computer Science at the University of Texas at Austin where he holds the W.A."Tex" Moncrief Chair of Computing. He is a Foreign Member of the Academia Europeana, a Distinguished Alumnus of IIT Kanpur, India, and a Fellow of the ACM, IEEE and AAAS. He has served on the NSF CISE Advisory Committee (2009-2012), and he was co-Editor-in-Chief of the ACM Transactions on Programming Languages and Systems (2007-2010). He is the author of more 200 papers in the area of graph computing, parallel and distributed systems, and programming languages.