Real-time Customer Engagement in Gaming Using Kafka and Flink

on YouTube ↗40m

" summary

Real pipeline detail: a game tracks interaction events into Kafka and Flink, evaluates campaigns via a JavaScript filter broadcast through a low‑frequency CRM topic, and writes triggers back to Kafka for the game to act on. The system processes about 1.5 billion events per day, uses broadcast state pattern and per‑player stateful segments, runs a Docker demo and open‑source GitHub repo, and achieves reaction times less than one second while supporting runtime rule updates with broadcast state pattern.

AI-written summary · about

" chapters (9)

1. 0:00Introduction
2. 3:16Speaker background
3. 6:50Stream processing in gaming
4. 10:47Kafka architecture
5. 17:06Flink basics
6. 23:24Customer engagement use case
7. 28:44Demo walkthrough
8. 36:51Future outlook
9. 38:58Closing

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