What are dbt Tags?

An In-depth Analysis

What are dbt Tags?

An Introduction to dbt

"dbt" stands for "data build tool." It's a software tool that has gained popularity among data analysts and engineers for transforming and modeling data in the data warehouse. Here are some key points about dbt:

  1. SQL-based: dbt uses SQL for defining transformations, which means analysts and engineers can leverage their existing SQL knowledge without needing to learn a new language or tool.
  2. Version Control: dbt integrates well with version control systems like Git, allowing teams to manage and track changes to their transformation logic over time.
  3. Testing and Documentation: dbt has built-in capabilities for data testing and documentation, ensuring data quality and understandability.
  4. Modularity: With dbt, you can break down your transformations into modular pieces (called "models"), making it easier to organize, understand, and maintain complex transformation workflows.
  5. Compilation: dbt compiles your transformation logic into raw SQL that runs directly against your data warehouse, ensuring high performance.
  6. Extensible: dbt has a rich ecosystem of plugins and extensions that can be used to integrate with various data platforms and tools.
  7. Open-Source: The core of dbt is open-source, though there is also a commercial version that provides additional features and support.
  8. Workflow: The general workflow with dbt involves defining your transformations as models, testing the output for data quality, and then deploying the transformations to production.

Overall, dbt offers a systematic and powerful way to handle the "transform" step in the ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) process, directly in the data warehouse environment.

What are dbt Tags?

Tags in dbt are a way to label or categorize models, tests, and other dbt artifacts. They provide a mechanism to add metadata to these artifacts.

Purpose of Tags

Here's a brief overview:

  • Organization: Tags can help in organizing and grouping similar models or tests together.
  • Execution Control: You can run specific sets of models or tests based on their tags. This can be especially useful when you have a large number of models and you want to run a subset of them.
  • Usage:To add a tag to a model in your schema.yml file, you might have something like this:
In the above example, the my_model model has been tagged with weekly and finance.
  • Execution Based on Tags:Once you've tagged your models, you can run or test a subset of them using the --select and --exclude flags combined with tag selectors. For instance:
The above command would run all models tagged with weekly.
  • Extensibility:
    - Custom tags can be created based on the needs of the project.
    - They can also be used in conjunction with dbt's documentation site to provide an additional layer of metadata to your models.

In summary, tags in dbt are a way to add metadata to models, tests, and other dbt resources. They allow for better organization and selective execution of these resources based on their associated tags.

dbt Tags: A Powerful Tool for Categorization

dbt Tags are primarily used as identifiers or labels assigned to various elements within a dbt project. These elements include models, tests, snapshots, analyses, and seed files. But the scope of dbt Tags extends beyond simple categorization. They provide a flexible way to organize dbt resources and control how those resources are selected for various operations.


The beauty of dbt Tags is their hierarchical nature. This means you can apply tags at different levels—model, source, or project—and they will behave accordingly. This is particularly useful when you have complex dbt projects with numerous resources. According to Yu Ishikawa’s article, the scope of a tag can vary based on where it's defined.

For example, a tag defined at the model level will apply to that specific model. If a tag is assigned at the source level, it applies to all data models associated with that source. A tag defined at the project level is even broader, applying to all resources within the project. This multi-level scoping allows for flexible resource grouping and targeted execution of dbt operations.

Operational Control: Running and Testing

Another critical aspect of the dbt Tags' scope is their role in running and testing operations. By using the 'dbt run' or 'dbt test' command followed by '--model tag:<tagname>', you can specifically run or test the models associated with a particular tag. This functionality provides a high level of operational control, allowing you to selectively execute dbt operations based on your current needs.

For example, if you have a group of models that need to run daily, you can tag these models as 'daily' and use the 'dbt run --model tag:daily' command to run them.

Similarly, you can group critical tests under a 'critical' tag and use the 'dbt test --model tag:critical' command line to run these tests regularly.


dbt Tags also play a role in project documentation. They can be used to categorize and organize documentation resources. Hence, making it easier for team members to find and understand the different elements of a dbt cloud project.

Some data catalogs, like CastorDoc, leverage dbt tags to auto-document or suggest documentation, group metrics, explain SQL code & much more.

Best Practices for Implementing dbt Tags

Be Consistent and Use Descriptive Tag Names

Consistency ensures that everyone understands the meaning of a tag, and descriptive names self-document the purpose of the models or tests. Check dbt's best practices.

In the example above, name is consistent with tags & dbt tags are self-explanatory.

Document Tag Usage

Clear documentation ensures that all team members know when and how to use specific tags, which enhances collaboration and understanding.

Tagging Guidelines

- `daily`: Used for models that aggregate or process data on a daily frequency.
- `monthly`: Used for models that aggregate or process data on a monthly frequency.
- `sales`: Indicates models related to sales data.
- `expenses`: Indicates models related to expense data.

Combine with Other dbt Selectors

Using tags in conjunction with other selectors offers greater flexibility and precision in controlling the execution of dbt artifacts.

This command ensures that only the models tagged with "daily" and "sales" and the model named "daily_sales_report" are executed, providing more precise control over what's run.

In summary, when used effectively, dbt tags can significantly transform your raw data processing workflow. They:

  • Introduce a high level of organization and control over source systems,
  • Streamline operations,
  • Allow selective task execution.

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