Abstract
Details coming soon.
Details coming soon.
Tuesday Mar 17 / 10:35AM GMT
What if Kafka brokers were ephemeral, stateless and leaderless with durability delegated to a pluggable storage layer?
Peter Morgan
Founder @tansu.io
Tuesday Mar 17 / 01:35PM GMT
ML training pipelines treat data as static. Teams spend weeks preprocessing datasets into WebDataset or TFRecords, and when they want to experiment with curriculum learning or data mixing, they reprocess everything from scratch.
Onur Satici
Staff Engineer @SpiralDB
Tuesday Mar 17 / 11:45AM GMT
Over the last decade, streaming architectures have largely been built around topic-centric primitives—logs, streams, and event pipelines—then stitched together with databases, caches, OLAP engines, and (increasingly) new serving systems.
Giannis Polyzos
Principal Streaming Architect @Ververica
Anton Borisov
Principal Data Architect @Fresha
Tuesday Mar 17 / 02:45PM GMT
As Netflix scales hundreds of client platforms, microservices, and infrastructure components, correlating user experience with system performance has become a hard data problem, not just an observability one.
Prasanna Vijayanathan
Engineer @Netflix
Renzo Sanchez-Silva
Engineer @Netflix
Tuesday Mar 17 / 05:05PM GMT