Netflix built an automated data canary system to ensure catalog data reliability. This system validates data transformations using production traffic. It detects issues in under 10 minutes, preventing corrupted data from reaching millions of viewers. A past incident revealed gaps in their resilience strategy when manual actions corrupted a data feed. This corrupted data caused manifest generation failures and playback issues. Existing code canaries were ineffective because no code had changed, only the data. The challenge was validating high-velocity data pipelines with short time constraints. They needed to validate the actual output consumed by clients, not just inputs. Production traffic was deemed essential for detecting real customer impact. Their solution involved a dedicated orchestrator pattern with permanent baseline and canary clusters. They extended their chaos platform, customizing thresholds and using sticky canaries. Behavioral metrics like Starts Per Second became their primary signal for detection. The system also incorporated robust edge case handling for production environments. Controlled failure injection experiments validated the system's effectiveness. Issues were identified in 2.5–4 minutes, with automatic blocking of corrupted data. This effort established that data deployments require the same rigor as code deployments.