Streaming Pipeline Readiness Checklist
A go-live checklist for real-time streaming pipelines on platforms like Kafka or Pulsar. It covers topic and schema design, delivery semantics, idempotent consumers, dead-letter handling, and lag monitoring. Use it to verify a stream is resilient and observable before production.
When to Use This Checklist
Use this checklist before taking a real-time streaming pipeline to production. Streaming systems built on Apache Kafka, Pulsar, or similar platforms process events continuously, which makes their failure modes different from batch jobs. Issues like consumer lag, duplicate delivery, and schema drift appear at runtime under load, so readiness must be verified before go-live.
How to Use This Checklist
Start with the structural decisions: topic design, partitioning, retention, and schema governance. These are hard to change once data is flowing. Then confirm delivery semantics and make consumers idempotent, because most streaming platforms deliver at least once and duplicates are normal. The resilience and observability items ensure you can see and recover from problems. Treat the security and dead-letter items as required; a streaming pipeline without them fails quietly and dangerously.
What Good Looks Like
A production-ready stream has governed schemas with enforced compatibility, documented delivery semantics, and idempotent consumers that shrug off duplicates. Poison messages land in a dead-letter queue with a replay path instead of stalling the pipeline. Consumer lag and end-to-end latency are monitored with alerts, and the system has been load-tested through rebalancing and backpressure. Topics are encrypted and access-controlled, and an offset-reset runbook lets on-call engineers recover quickly.
Common Pitfalls
The most common mistake is assuming exactly-once delivery when the platform provides at-least-once, leading to duplicate processing. Unmanaged schema evolution breaks consumers when producers change event shapes. Pipelines without dead-letter handling halt on a single bad message. Teams often skip load testing of rebalancing and backpressure, then see cascading lag in production. Finally, missing consumer-lag monitoring means a stalled pipeline is discovered only when downstream data goes stale.
Related Resources
Ground the design in apache-kafka-streaming-best-practices and enforce compatibility with schema-evolution-schema-registry and data-contracts. Add distributed-tracing-best-practices and service-level-objectives-slos so latency and reliability are measured, not assumed.