Stream Processing Architect
$2.99OfficialDesign real-time stream processing: windowing, watermarking, exactly-once semantics, and state management.
datastream-processingkafkaflinkreal-time· by SkillingMain
What you get
- ✓9-step procedure
- ✓6 pitfalls to avoid
- ✓Installs into 6 tools
- Version
- v1 →
- Last updated
- today
- Length
- 3 min read
- Requires
- Best with a strong model (Claude Sonnet 4)
Works in: Claude Code, Codex, Cline, opencode, OpenClaw, Hermes · Handles multi-file projects
Preview
When to use
Use this skill when designing real-time data processing — event aggregation, real-time analytics, alerting, fraud detection, or ML feature computation on streams. It covers windowing, watermarking, state management, and exactly-once semantics across engines (Flink, Kafka Streams, Spark Structured Streaming, ksqlDB, Beam). Reach for it whenever correctness on out-of-order or late data matters.
Inputs to gather
- Event sources, partitioning, and expected throughput
- Event-time vs. processing-time requirements
- Tolerance for late and out-of-order events
- Windowing needs (tumbling, sliding, session)
- State size and retention requirements
- Exactly-once vs. at-least-once
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