Analyzing geospatial data is one of Uber’s distinct challenges; city locations, trips, and event information, for instance, provide insights that can improve business decisions and better serve users. Geospatial data analysis is particularly challenging, especially in a big data scenario, such as computing how many rides start at a transit location, how many drivers are crossing state lines, and so on. For these analytical requests, Uber must achieve efficiency, usability, and scalability in order to meet user needs and business requirements.
From determining the most convenient rider pickup points to predicting the fastest routes, Uber aims to use data-driven analytics to create seamless trip experiences. Within engineering, analytics inform decision-making processes across the board. Zhenxiao Luo explains how Uber uses artificial intelligence in its production environment to process the big data powering its interactive SQL engine.
Zhenxiao Luo is leading Interactive Query Engines team at Twitter, where he focuses on Druid, Presto, and Hive. Before joining Twitter, Zhenxiao was running Interactive Analytics team at Uber. Previously, he worked on big data related projects at Netflix, Facebook, Cloudera, and Vertica. Zhenxiao is PrestoDB committer. He holds a master’s degree from the University of Wisconsin-Madison and a bachelor’s degree from Fudan University.
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