Data quality has shifted from a checkpoint to being an operational requirement. As more and more data warehouses become cloud-native, and the complexity of running real-time pipelines increases, data engineers face a non-trivial problem: how to operationalize quality checks without slowing down the velocity of the ETL workflows. “Traditional post-load checks or static rules” do not suffice. Automated validation and anomaly detection in cloud ETL pipelines need to be performed in a manner that adapts to evolving schemas, variable latency, and dynamic business logic.

Why Reactive Data Quality Is No Longer Enough

In the past, data quality was typically validated at the end of an ETL pipeline, often using standalone validation scripts or manual dashboards. This post-hoc approach worked reasonably well in static, batch-oriented data ecosystems. However, in modern cloud environments where data flows through event-driven, streaming, and micro-batch jobs, such passive controls introduce significant latency and operational risk. By the time an issue is detected — sometimes hours or even days later — the damage may already be done.