Patrick Ames

For data-intensive workloads such as ML training, analytics, and AI search, every millisecond spent waiting on data costs money without advancing the work. When workloads make millions of small, random reads and writes, low-latency storage keeps compute productive without provisioning for peak demand.

Production case study highlighting how Amazon uses Ray and DeltaCAT at exabyte-scale to resolve longstanding performance & scale challenges integrating streaming pipelines with Apache Iceberg. Highlights how the Apache Beam, Ray, Apache Flink, and Apache Spark communities can start bringing the...

The Ray Beam Runner Project is an initiative to create a new Pythonic Beam Runner based on the Ray distributed compute framework. The project was conceived based on strong community interest in integrating Ray with Beam, and a prototype quickly proved its viability. We will discuss the current...