EventLens is a strong example of learning distributed systems by building a complete product instead of studying each component in isolation. Bringing Kafka, Kubernetes, Redis, PostgreSQL, SDKs, metrics, traces, and logs into one platform makes the architectural trade-offs much easier to understand.

I especially appreciate the focus on observability and real system pressure, not just the final dashboard. In an analytics platform, ingestion latency, duplicate events, failed deliveries, schema evolution, and high-cardinality data can all affect the accuracy of the insights. It would be interesting to see a future article covering delivery guarantees, event deduplication, and how EventLens handles schema changes across different SDK versions. Excellent conclusion to a practical and technically valuable series.