Flink
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System Design
Flink
A stream-processing engine for stateful real-time computation over event streams with event-time semantics.
🚧 This page is still under construction. More content and notes will be added soon.
Notes
Key Points
- Flink is used when you need continuous computation over streams rather than batch processing after the fact.
- It is strong for windowed aggregations, fraud detection, anomaly detection, and other cases where timing and state both matter.
- Event time, watermarks, and stateful operators are the ideas worth knowing at interview level.
- Flink usually sits downstream of a broker like Kafka and produces derived streams or materialized outputs.
Interview Focus
Mention Flink when real-time stream processing is actually the problem, not just because Kafka is present. The value is in stateful computation over streams.
