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Broken Windows of Data
Data warehouse decay is a technical debt issue that requires shifting quality checks into the earliest phases of the development lifecycle.
What: Booking.com engineers argue that data quality must be treated like code quality, moving away from post-hoc monitoring toward modeling reviews, CI/CD validations, and automated testing at the point of ingestion.
Why it matters: Data platforms often reach a 'broken window' state where minor inconsistencies proliferate into untrustworthy business logic. This reflects the maturation of data engineering toward professional software engineering standards.
Takeaway: Integrate data modeling reviews and automated validation tests into your existing GitHub/GitLab CI/CD pipeline before merging to main.
Original article
Data warehouse quality needs to be built into the full development lifecycle, not left to final review. Teams can stop small inconsistencies from spreading into shared definitions, dashboards, and business logic by shifting checks earlier through modeling reviews, local validation, CI/CD, AI-assisted review, human judgment, and monitoring.
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