⚡AgentSkills
🛢️ Data Engineering · Streaming & Modeling

Choose windowing and watermarks correctly

Event-time vs processing-time decisions, late-data handling, and exactly-once semantics.

advanced~40 minData EngineersAnalytics EngineersPlatform Data Teams

Steps

  1. 1Timestamp on event time extracted from payload, not arrival
  2. 2Set watermark delay from measured lateness distribution p99, not guesswork
  3. 3Decide allowed-lateness policy: update vs side-output for stragglers
  4. 4Key by cardinality you can hold state for; watch idle-key eviction
  5. 5Verify exactly-once end-to-end through failure injection tests
  6. 6Monitor watermark lag as the primary stream health metric

Common Pitfalls

  • ▲Processing-time windows rewriting history on replays
  • ▲Unbounded state growth from high-cardinality keys

Commands

Install with skills CLI
$ npx skills add aniruddhaadak80/skills --skill streaming-modeling-stream-processing-windows
Install globally
$ npx skills add aniruddhaadak80/skills --skill streaming-modeling-stream-processing-windows -g

Tags

#streaming#kafka#flink#data-engineering#streaming-modeling

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