How does dbt handle the documentation of data models?

dbt generates documentation for data models by utilizing YAML file comments and model configurations, which is a key feature of its capability to create a robust data documentation site. This process allows users to provide descriptions and metadata directly in their dbt model files through comments. When you run a dbt command to generate documentation, dbt compiles all this information into an interactive documentation site that is easy to navigate and understand.

The integration of documentation with the model configurations simplifies the workflow for analytics engineers. It ensures that documentation is not just adjunct information but is closely tied to the data models and their attributes, making it more relevant and readily accessible.

This feature stands apart from other approaches that may involve separate documentation processes or tools. Automatic creation of documentation pages during model execution, while appealing, does not leverage the detailed insights offered by structured metadata. Similarly, requiring manual entry in separate Markdown files can lead to inconsistencies and lag in keeping documentation up-to-date with changes in the models. Lastly, using external tools to document data models would detach the documentation from the dbt workflow, which could lead to complications or discrepancies.