Data teams often face challenges in maintaining reliable and consistent data due to growing pipelines and complex workflows, which can lead to production issues. A shift-left approach, incorporating data quality checks earlier in the development process, is recommended to address these challenges effectively. Data diffing, as practiced by Datafold, is a method to proactively detect and resolve data discrepancies before they reach production, ensuring the accuracy and efficiency of data pipelines. Best practices for integrating data diffing into CI/CD pipelines include handling large datasets and optimizing performance using strategies like Slim Diff and sampling. For dbt projects, Slim CI can be configured to build only modified models and their downstream dependencies, enhancing efficiency. Datafold's Slim Diff feature further optimizes performance by focusing only on models with direct code changes, thereby reducing runtime and costs, especially in projects with complex Directed Acyclic Graphs (DAGs).