Event-driven architecture sounds elegant in theory. In practice, it requires careful design to avoid the common pitfalls of distributed systems.
Events as First-Class Citizens
The key insight is treating events not as side effects, but as the primary interface between system components. When events are first-class, everything else follows. This principle drove the design of the Real-Time Analytics Pipeline, where Kafka topics became the source of truth and batch ETL became a historical artifact — sub-800ms event-to-dashboard latency replaced 12-hour overnight batch runs.
Schema Registry
Without a schema registry, event-driven systems degrade into chaos. Every event must have a versioned schema that consumers can validate against. The Schema Evolution Engine exists because I learned this lesson the hard way: unversioned schema changes in a distributed pipeline produce failures that are invisible until a downstream consumer breaks at 3 AM.
The data quality lessons from event-driven work also apply to batch pipelines — the SEC Filings post covers the validation-first mindset from a different angle, where a 58% false positive rate in 36,791 filings proved that volume without validation is noise. And the broader argument for mature, well-understood infrastructure over novel alternatives lives in The Case for Boring Technology.
Related
Projects: Real-Time Analytics Pipeline · Schema Evolution Engine · SEC §127 Intelligence Suite
Writing: Building a Data Intelligence Pipeline from SEC Filings · The Case for Boring Technology
