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Learn about dbt-score, an open-source linting tool for dbt metadata, in this 31-minute conference talk from EuroPython 2025. Discover how to maintain high-quality data models by implementing continuous integration practices for dbt (Data Build Tool) metadata management. Explore the challenges of managing millions of metadata values in large dbt projects, including ensuring table testing adequacy, identifying sensitive data exposure, and enforcing column naming conventions. Understand the importance of declarative, git-tracked metadata and why linting this metadata is crucial for data quality, security, and compliance. Get hands-on insights into how dbt-score provides flexible enforcement of data team best practices through its CLI, enabling data practitioners to obtain maturity scores for their data models, manage metadata chaos, improve consistency, and deliver high-quality data at scale.
