Why Spark on Hadoop Matters - Databricks

Why Spark on Hadoop Matters

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The Hadoop technology stack, from its roots in batch processing, has evolved at a rapid pace to address more real-time business needs. Spark with an in-memory processing framework provides a great complimentary stack to Hadoop. Not surprisingly, the integration of the full Spark stack on Hadoop is showing tremendous promise for MapR customers. I will present some of these use cases and discuss how and when the integration of Spark and Hadoop delivers the best value for the end user.

About M.C. Srivas

Srivas 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. Srivas was Chief Architect at Spinnaker Networks (now NTAP) which built the industry’s fastest single-box NAS filer, as well as the industry’s most scalable clustered filer. Previously, he managed the Andrew File System (AFS) engineering team at Transarc (now IBM). AFS is now standard classroom material in operating systems courses. While not writing code, Srivas enjoys playing tennis, badminton and volleyball. M.C. has an MS in Computer Science from University of Delaware, and a B.Tech. in electrical engineering from IIT Delhi.