AI Briefing#stream-processing
The latest AI and developer news about #stream-processing, with the original source and a short summary.
The latest AI and developer news about #stream-processing, with the original source and a short summary.
The new Flink-based pipeline processes hundreds of thousands of events per second during live events with negligible errors, eliminating single points of failure in ad serving.
The mechanism uses Iceberg snapshot summaries to publish non-regressing watermarks, allowing downstream jobs to safely consume data from streaming pipelines.
Netflix is transitioning to an open-source solution from the Apache Flink community to overcome the limitations of its in-house autoscaler.
Apache Flink and RocksDB tuning enabled real-time ad impression aggregation from 1 minute to 7 days.
A method to narrow down Flink latency bottlenecks by separating processing time from non-processing time.
Kakao's Data Analytics Platform team detailed their production implementation of Apache Flink CDC to minimize database load while ensuring real-time data consistency.
Drawing on the Ad Recommendation Team's experience with real-time data processing, the session introduced case studies on applying reliability options such as Exactly-Once.