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Get the guidedbt, or Data Build Tool, is capable of automatically generating documentation for data models. This documentation can be rendered as a website, providing an accessible platform for data teams to reference. The documentation includes comprehensive information about the project, such as the model code, Directed Acyclic Graphs (DAGs), and column tests. It also provides details about the data warehouse, including column data types and table sizes.
dbt docs generate
This command is used to generate documentation for dbt models. It collects metadata from your project and compiles it into a website that can be served locally or hosted elsewhere.
Documentation serves as an essential resource for data teams. It acts as a reference library, providing technical details, tools, and methods for working with data. Documentation helps data teams organize data, track important data characteristics, and discover analytics. Clear, comprehensive documentation can enhance efficiency and reduce the time spent trying to understand the data structure.
The importance of documentation in data teams underscores the need for clear, concise, and accurate comments and descriptions within the codebase.
dbt employs "self-documenting" code. This means that the code written for models, tests, and other configurations also serves as documentation. This approach reduces the gap between code creation and documentation, ensuring they evolve together. It makes the documentation process more efficient and ensures that the documentation is always up-to-date with the latest code changes.
{
"models": {
"my_new_model": {
"description": "This is a description of my new model",
"columns": {
"column_1": {
"description": "This is a description of column_1",
"tests": ["unique", "not_null"]
}
}
}
}
}
This is an example of a dbt model configuration file. The "description" fields serve as self-documenting code, providing context and explanations for the model and its columns. The "tests" field lists the tests applied to the column, serving as documentation of the data quality checks in place.
In dbt Cloud, users have the ability to auto-generate documentation when their dbt project runs. This feature allows for real-time updates to the documentation as changes are made to the project. Users can also write, version-control, and share documentation for their dbt models by writing descriptions for each model and field in plain text or markdown.
dbt docs serve
This command is used to serve the generated documentation on a local web server. It allows users to view and share the documentation in a user-friendly format.
dbt allows users to add descriptions to models, columns, and sources to enhance the documentation. These descriptions can provide additional context and clarity, making the documentation more useful and understandable. They can be written in plain text or markdown, allowing for formatting and linking to further enhance the documentation.
{
"models": {
"my_new_model": {
"description": "This is a description of my new model",
"columns": {
"column_1": {
"description": "This is a description of column_1"
}
}
}
}
}
This is another example of a dbt model configuration file. The "description" fields provide additional context and explanations for the model and its columns, enhancing the usefulness and clarity of the documentation.
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