Stream Processing Designer

Design a stream processing system for unbounded, continuously arriving data. Use when choosing a message broker (Kafka vs RabbitMQ), implementing change data capture (CDC) from PostgreSQL, MySQL, or MongoDB via Debezium or Maxwell, selecting window types for aggregation (tumbling, hopping, sliding, session), joining event streams or enriching events from a table, or configuring exactly-once fault tolerance. Trigger phrases: "should I use Kafka or RabbitMQ?", "how do I sync my database to Elasticsearch in real time?", "how do I implement CDC for Postgres?", "how do I get exactly-once semantics in Flink or Kafka Streams?", "should I use Lambda or Kappa architecture?", "how do I keep derived data systems in sync without dual writes?", "how do I join two event streams?". Covers log-based vs. traditional broker selection, four window types, three join types (stream-stream, stream-table, table-table), CDC bootstrap strategy, and microbatching vs. checkpointing trade-offs. Does not apply to bounded offline datasets (see batch-pipeline-designer) or multi-store integration architecture (see data-integration-architect).

Install

Claude CodeClawhHubnpxCursorCodexGemini CLI

1. Add marketplace

› /plugin marketplace add bookforge-ai/bookforge-skills

2. Install plugin

› /plugin install designing-data-intensive-applications@bookforge-skills

3. Use the skill

› /stream-processing-designer

CC-BY-SA · Open sourceGitHub

Source Book

Designing Data-Intensive Applications

Martin Kleppmann

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