Kafka Streaming taste

# Kafka Streaming taste of a pragmatic event platform engineer
- Name topics by domain and event, such as `orders.order-placed.v1`, and put the schema version in the name only when a breaking change forces a new topic. Confidence: 0.85
- Register every topic schema in a schema registry (Avro or Protobuf) with backward compatibility enforced; never publish raw untyped JSON to shared topics. Confidence: 0.90
- Choose the partition key from the entity whose ordering matters (order id, account id), and never rely on ordering across partitions. Confidence: 0.95
- Start with enough partitions for expected peak consumer parallelism plus headroom, because increasing partitions later breaks key-to-partition mapping. Confidence: 0.80
- Run producers with `acks=all`, idempotence enabled and `min.insync.replicas=2` on a replication factor of 3 for anything business-critical. Confidence: 0.90
- Treat delivery as at-least-once and make every consumer idempotent with a dedupe key or upsert, instead of chasing end-to-end exactly-once. Confidence: 0.90
- Commit offsets only after the side effect has succeeded; disable auto-commit for consumers that write to databases or call APIs. Confidence: 0.90
- Send messages that fail repeatedly to a dead-letter topic with the original headers, error and attempt count, and alert on its growth. Confidence: 0.85
- Retry transient failures with bounded backoff inside the consumer before dead-lettering; never block a partition forever on one poison message. Confidence: 0.85
- Keep events as facts in past tense that describe what happened, not commands telling another service what to do. Confidence: 0.80
- Keep payloads small and self-contained enough to process without a synchronous call back to the producer, but put large blobs in object storage and send a reference. Confidence: 0.75
- Use the transactional outbox pattern to publish events atomically with database writes instead of dual writes. Confidence: 0.90
- Use log compaction for topics that represent the latest state per key, and time-based retention for pure event streams. Confidence: 0.85
- Monitor consumer lag per group and partition as the primary health signal, alerting on sustained growth rather than single spikes. Confidence: 0.90
- Put a correlation id and the producing service name in message headers so traces can follow an event across services. Confidence: 0.80
- Give every consumer group a stable, descriptive id tied to the service, and never share one group between unrelated applications. Confidence: 0.85
- Reach for Kafka Streams or Flink only when you need stateful joins or windowing; a plain consumer is simpler for stateless transforms. Confidence: 0.75
- Manage topics, partitions, retention and ACLs as code reviewed in pull requests, and disable automatic topic creation in production. Confidence: 0.90
- Lock down clusters with TLS, authenticated clients and per-service ACLs that grant only the topics each service needs. Confidence: 0.85
- Test consumers against a real broker in containers for integration tests, and keep unit tests on pure handler functions. Confidence: 0.80
- Document each topic's owner, schema, key, retention and consumers in a catalog that lives next to the topic definitions. Confidence: 0.70