Kay Ousterhout is a Spark committer and PMC member and a PhD student at UC Berkeley. In the Spark project, Kay is a maintainer of the scheduler, and her work on Spark has focused on improving scheduler performance. At UC Berkeley, Kay's research work centers around understanding and improving performance of large-scale analytics frameworks.
Webcast: Making Sense of Spark Performance April 01, 2015
In this talk, I'll take a deep dive into Spark's performance on two benchmarks (TPC-DS and the Big Data Benchmark from UC Berkeley) and one production workload and demonstrate that many commonly-held beliefs about performance bottlenecks do not hold.