The talk will explore Uber’s business problems, and how they are related to real-time analytics of big-data. How do we at Uber figure out what data to collect, what is missing and what analysis is relevant at what stage of the operations. We discuss the technologies underpinning the big-data architecture at Uber in the context of some of the real-time problems Uber needs to solve to make ride sharing as smooth and as ubiquitous as running water. We also look at some of the big data challenges with autonomous vehicles, especially regarding what it takes to have a self-driving car running on the road safely.
M. C. Srivas
Chief Data Architect
Srivas is now the Chief Data Architect of Uber. Prior to this role, Srivas is the CTO and co-founder of MapR Technologies. He also ran one of the major search infrastructure teams at Google where GFS, BigTable and MapReduce were used extensively. He wanted to provide that powerful capability to everyone, and started MapR on his vision to build the next-generation platform for semi-structured big data. His strategy was to evolve Hadoop and bring simplicity of use, extreme speed and complete reliability to Hadoop users everywhere, and make it seamlessly easy for enterprises to use this powerful new way to get deep insights. That vision is shared by all at MapR. Srivas brings to MapR his experiences at Google, Spinnaker Networks, Transarc in building game-changing products that advance the state of the art.