Opening a pull request to modify dbt models can be stressful, as even successful CI pipeline runs and passing dbt tests may not reveal all potential data changes introduced by the modifications. While dbt tests are designed to catch certain data quality issues, they may not cover all scenarios, potentially leading to broken dashboards and malfunctioning pipelines if unexpected data changes occur. Datafold is introduced as a complementary tool to dbt tests, providing additional test coverage by identifying value-level differences between staging and production data. This helps prevent issues such as errors in data values, distribution shifts, and missing table sections that dbt tests might miss. Therefore, integrating Datafold into CI pipelines alongside dbt tests is recommended to ensure the accuracy, completeness, and consistency of data, ultimately safeguarding data quality when deploying code changes to production.