How to dbt check

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Last updated: April 4, 2026

Quick Answer: To 'dbt check,' you typically refer to running checks on your dbt project's code and data integrity. This involves executing commands like `dbt test` to validate model logic and data quality, and `dbt docs generate` to create documentation. These checks ensure your data transformations are accurate and your project is well-documented.

Key Facts

dbt tests are defined in YAML files and run using the `dbt test` command.
There are built-in generic tests (e.g., unique, not_null, accepted_values, relationships) and custom tests.
Data quality checks are crucial for ensuring the reliability of your dbt models.
Documentation generation with `dbt docs generate` is a key part of understanding your dbt project.
Running `dbt compile` first can help catch syntax errors before running tests.

What is dbt?

dbt (data build tool) is a transformation workflow tool that enables data analysts and engineers to transform data in their warehouse more effectively. It empowers teams to collaborate on data models, test data quality, and deploy documentation. dbt operates on the principle of "analytics engineering," where SQL transformations are treated as code, managed with version control, and deployed automatically.

Understanding 'dbt check'

The phrase 'dbt check' is not a single, explicit command within the dbt CLI. Instead, it refers to the process of verifying the health, correctness, and quality of your dbt project. This encompasses several key activities:

1. Running Tests (`dbt test`)

The most direct way to 'check' your dbt project is by running tests. dbt tests are assertions about your data that help you detect errors. They are defined in your dbt project, typically in YAML files, and executed using the `dbt test` command.

Types of dbt Tests:

Generic Tests: dbt provides a set of built-in, configurable tests that can be applied to your models. These include:
- unique: Ensures that all values in a column are unique.
- not_null: Verifies that a column contains no null values.
- accepted_values: Checks if values in a column are within a predefined set of acceptable values.
- relationships: Validates that foreign key relationships between tables are maintained.
Singular Tests: These are custom SQL queries that you write yourself to perform more complex data quality checks. They are defined in `.sql` files within your `tests` directory.

Running `dbt test` is fundamental to ensuring data integrity. It helps catch issues like duplicate primary keys, missing essential data, or incorrect referential integrity before bad data propagates downstream to dashboards and reports.

2. Generating Documentation (`dbt docs generate`)

A well-documented dbt project is easier to understand, maintain, and use. The `dbt docs generate` command compiles your project's metadata and your SQL comments into a shareable website. This documentation includes:

Model descriptions: Explanations of what each model does.
Column descriptions: Details about individual columns within models.
Test descriptions: Explanations of the tests applied to your data.
Lineage: Visualizations showing how models depend on each other, helping to understand data flow.

While not a direct data check, generating documentation is a crucial 'check' on the overall understandability and maintainability of your project. It ensures that other users (or your future self) can easily grasp the purpose and functionality of your data models.

3. Compiling the Project (`dbt compile`)

Before running tests or generating documentation, it's often a good practice to compile your dbt project using `dbt compile`. This command translates your dbt project's Jinja and SQL into plain SQL, saving it to the `target/compiled` directory. This step can help you catch syntax errors in your SQL or Jinja templating before attempting more resource-intensive operations like testing or running models.

4. Running Models (`dbt run`)

While `dbt run` primarily builds your data models in the warehouse, it also implicitly performs checks. If a model fails to build due to SQL errors or data issues (like division by zero), `dbt run` will fail. Therefore, a successful `dbt run` is a basic form of checking that your models can execute without fundamental errors.

Best Practices for 'dbt Check'

Automate Tests: Integrate `dbt test` into your CI/CD pipeline to automatically check data quality on every code change.
Comprehensive Testing: Implement a mix of generic and custom tests to cover various data quality dimensions.
Regular Documentation Updates: Ensure your documentation is up-to-date by running `dbt docs generate` regularly, especially after making changes to models or tests.
Version Control: Use Git or another version control system to track changes to your dbt project, including tests and documentation.
Monitor Test Results: Pay attention to test failures. Investigate the root cause and fix the underlying data issues or model logic.

In summary, 'dbt check' is a multifaceted process involving running tests for data integrity, generating documentation for clarity, and ensuring your models compile and run successfully. These practices are essential for building a reliable and maintainable data transformation pipeline.