How does Uber figure out what data to collect, what data is missing, and what analysis is relevant at what stage of the operations?
M. C. Srivas covers the technologies underpinning the big data architecture at Uber and explores some of the real-time problems Uber needs to solve to make ride sharing as smooth and ubiquitous as running water, explaining how they are related to real-time big data analytics. Along the way, M. C. looks at some of the big data challenges with autonomous vehicles, especially regarding what it takes for a self-driving car to run on the road safely, and discusses the many applications within Uber that rely on data science and AI technologies.
M.C. Srivas is an Architect at Bridgewater. Previously, M. C. was CTO and founder of MapR Technologies, a top Hadoop distribution; worked on search at Google, developing and running the core search engine that powered many of Google’s special verticals like ads, maps, and shopping; was chief architect at Spinnaker Networks (now Netapp), which formed the basis of Netapp’s flagship NAS products; and ran the Andrew File System team at Transarc, which was acquired by IBM. M. C. holds an MS from the University of Delaware and a BTech from IIT-Delhi.
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