Within open-source knowledge introductions. Deep checklist: Event streaming & automation best practices—boundaries between NATS and Kafka workloads, automation guardrails, observability expectations.
Throughput is not the first question
Teams sometimes choose Kafka by reputation when domain boundaries and replay contracts are still fuzzy. NATS (including JetStream) can be simpler where clear subjects, low-friction operations, and speed matter—provided your failure semantics fit. The decision belongs to workload shape and team skill—not conference keynotes.
Automation without policy remains brittle
n8n can stitch APIs quickly, but retries, secrets rotation, and dead-letter behaviour must be designed. Treat workflows like services: ownership, monitoring, change logs.
Delivery links
Related introductions
Analytics freshness assumptions: Analytics & BI. Agent/tool orchestration overlaps integrations—LLM agents & orchestration.
Trademark notice
Names for orientation only.
Think streaming and automation together
We help with load profiles, integration patterns, and sustainable operations.
