Why Apify's Success Status Lies: Detecting Hidden Data Quality Failures

Apify's native monitoring reports runs as 'succeeded' or 'failed,' but silently ignores data quality issues—unexpected fields dropped, null rates spiking, field mismatches lurking undetected. Most production failures go undetected, sometimes for weeks, damaging data integrity and revenue. An 8-stage execution lifecycle framework with deterministic decision enums fills this gap, providing actionable quality validation at every phase from input through deployment.