Modern data platforms are increasingly responsible for powering critical decisions, applications, analytics systems, and AI workloads. As organizations move more business processes onto data-driven architectures, the reliability of data pipelines becomes as important as the reliability of application services.A pipeline that fails loudly is usually easy to diagnose. A failed job, broken dependency, or unavailable service typically generates an alert and directs engineers toward a specific issue. The more difficult problems are the ones that do not interrupt execution at all. A pipeline can complete successfully while producing incorrect, incomplete, or misleading data.