Up to this point, we have defined the logic for data quality assertions and established the metrics required for observability. However, defining these checks is effective only if they execute consistently. Relying on manual validation or ad-hoc scripts creates a point of failure in the engineering process. This chapter shifts the focus to the automated execution of these protocols within the Continuous Integration (CI) environment.

We will examine the technical implementation of reliability checks throughout the software development lifecycle. The discussion begins with pre-commit hooks, which sanitize Python and SQL code locally before it enters the repository. We then progress to server-side quality gates that block non-compliant code from reaching production. You will also learn to implement circuit breakers, which are mechanisms that halt pipeline execution when data metrics deviate from acceptable ranges. For instance, if the error rate $E$ in a batch ingestion exceeds a defined threshold $\tau$ (expressed as $E > \tau$), the system must automatically terminate the process to prevent downstream contamination. Finally, we cover alerting strategies designed to reduce noise and direct engineering attention to genuine incidents.