Taking your dbt CI pipeline to the next level

The text discusses the progression from a basic Continuous Integration (CI) pipeline for dbt Core projects to a more advanced setup that incorporates four key integrations to enhance efficiency, error resistance, and collaboration. Initially, a simple CI workflow was established with a minimal codebase to install Python and dbt, compile, build, and test dbt models, and run a SQL linter on new code. The advanced setup introduces Slim CI, which optimizes the process by building and testing only modified models and their downstream dependencies. Additionally, the text covers the use of hooks for task automation, Data Diffs for effective change tracking, and Slack notifications for real-time team updates. These features are recommended for scalable data governance and are actively employed by Datafold in their repositories to maintain robust and efficient data processes. The text also explains the necessity of a manifest.json file for implementing Slim CI, highlighting its importance as data teams grow and manage increasingly complex projects.

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