Advanced DBT Techniques: Macros, Testing, and Documentation

Are you ready to take your DBT skills to the next level? Do you want to learn advanced techniques that will make your data transformations even more efficient and effective? If so, then you've come to the right place!

In this article, we'll explore three advanced DBT techniques that will help you streamline your workflow and produce high-quality results. These techniques are macros, testing, and documentation.

So, let's get started!


Macros are one of the most powerful features of DBT. They allow you to create reusable code snippets that can be called from within your data models. This can greatly simplify your code and make it easier to read and maintain.

To create a macro, you simply define it in a separate file and then reference it from within your model. Here's an example:

-- macros/my_macro.sql
{% macro my_macro() %}
FROM my_table
WHERE created_at >= DATE_TRUNC('week', CURRENT_DATE)
{% endmacro %}
-- models/my_model.sql
FROM {{ my_macro() }}

In this example, we've defined a macro called my_macro that selects data from a table based on the current week. We then reference this macro from within our model using the {{ my_macro() }} syntax.

By using macros, we can avoid duplicating code and make it easier to maintain our models. If we need to update the logic of our macro, we only need to do it in one place.


Macros can also take parameters, allowing them to be more flexible. Here's an example:

{% macro my_macro(date_trunc) %}
FROM my_table
WHERE created_at >= {{ date_trunc }}(CURRENT_DATE)
{% endmacro %}

In this example, we've added a parameter called date_trunc, which specifies the date truncation function we want to use. We can then pass in a different date truncation function depending on our needs.

{{ my_macro('week') }}
{{ my_macro('month') }}
{{ my_macro('year') }}

By using parameters in our macros, we can make them more adaptable to different scenarios and avoid having to create multiple macros with similar logic.


Testing is an important part of any software development process, and DBT is no exception. DBT provides a testing framework that allows you to test your data models and ensure that they're producing the expected results.

To write tests in DBT, you simply create a separate file that defines the tests you want to run. Here's an example:

-- models/my_model.sql
FROM my_table
WHERE created_at >= DATE_TRUNC('week', CURRENT_DATE)
-- tests/my_model.yml
version: 2
  - name: my_model_has_data
    query: SELECT COUNT(*) FROM {{ ref('my_model') }}
    threshold: 0

In this example, we have a model that selects data from a table based on the current week. We then define a test that ensures that the model produces at least one row of data.

By writing tests for our models, we can ensure that they're producing the expected results and catch any issues early on. This can save us a lot of time and headaches in the long run.

Additional Tests

DBT provides several other types of tests that can be used to validate your data models. These include:

By using these tests, we can ensure that our models are not only producing the expected results, but are also being built correctly and conforming to our business logic.


Documentation is an often overlooked aspect of software development, but it's crucial for maintaining code and ensuring that others can understand it. DBT provides several features that make it easy to document your data models.


One of the simplest ways to document your data models is to use descriptions. Descriptions provide a way to add a comment to a model or column that explains its purpose or meaning.

-- models/my_model.sql
-- A description of my_model
FROM my_table
WHERE created_at >= DATE_TRUNC('week', CURRENT_DATE)
-- models/my_model.sql
  -- A description of my_column
FROM my_table
WHERE created_at >= DATE_TRUNC('week', CURRENT_DATE)

By using descriptions, we can provide context for our models and columns and make it easier for others to understand what we're trying to accomplish.


DBT also provides a docs command that can generate documentation for your data models. This command generates a markdown file that includes information about each model, column, and test.

To use the docs command, you simply run dbt docs generate in your terminal. This will generate a index.html file that you can open in your browser to view the documentation.

By using the docs command, we can create documentation that is easy to read and navigate, and provides a comprehensive view of our data models.


In this article, we've explored three advanced DBT techniques: macros, testing, and documentation. By using these techniques, we can create more efficient, reliable, and maintainable data transformations.

Macros allow us to create reusable code snippets that simplify our code and make it easier to maintain. Testing allows us to ensure that our data models produce the expected results and catch any issues early on. Documentation provides context for our code and makes it easier for others to understand and work with.

By incorporating these techniques into our workflow, we can create high-quality data transformations that meet our business needs and provide value to our organizations.

Are you excited to start using these advanced DBT techniques in your own work? We hope so! Happy coding!

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