A feature store is only as reliable as the pipeline feeding it.

If a batch job fails or runs with stale data, the system doesn’t break. It keeps serving old values, and the model continues running with slightly degraded inputs. The impact is subtle and often goes unnoticed until metrics drift over time.

Data pipelines are what prevent this. They move raw data through transformations into feature stores and training datasets, with clear guarantees around freshness, correctness, and scheduling.