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dbt-checks is a lightweight library of reusable data quality checks for dbt projects.

It provides simple, expressive tests to validate business rules and data integrity directly in your models — without writing custom SQL every time.

⚠️ Early-stage project — feedback and contributions are welcome.

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Installation

Add the package to your packages.yml:

packages:
  - git: https://github.com/LlucMH/dbt-checks.git
    revision: v0.2.4

Then install dependencies:

dbt deps

💡 Always pin a version in production projects.

Usage

Checks can be added directly to models or columns in your schema files.

Example:

models:
  - name: orders
    columns:
      - name: value
        data_tests:
          - dbt_checks.non_negative
          - dbt_checks.between_values:
              arguments:
                min_value: 0
                max_value: 10000

Run tests as usual:

dbt test

Available Checks

dbt-checks provides reusable data validation tests grouped by category.

Numeric

Numeric checks validate numeric ranges and thresholds.

Check	Description
non_negative	Ensures values are ≥ 0
non_positive	Ensures values are ≤ 0
greater_than	Ensures values are greater than a threshold
greater_or_equal_than	Ensures values are ≥ a threshold
less_than	Ensures values are less than a threshold
less_or_equal_than	Ensures values are ≤ a threshold
between_values	Ensures values fall within a numeric range

Example

columns:
  - name: value
    data_tests:
      - dbt_checks.between_values:
          arguments:
            min_value: 0
            max_value: 100

String

String checks validate textual fields such as identifiers or formatted values.

Check	Description
not_blank	Ensures strings are not empty or whitespace
length_between	Validates string length range
matches_regex	Validates a regex pattern
starts_with	Ensures string starts with prefix
ends_with	Ensures string ends with suffix
contains	Ensures string contains substring

Example

columns:
  - name: email
    data_tests:
      - dbt_checks.matches_regex:
          arguments:
            pattern: "^[^@]+@[^@]+\\.[^@]+$"

Temporal

Temporal checks validate date and timestamp fields.

Check	Description
not_future_date	Ensures date is not in the future
not_before_date	Ensures date is after a minimum date
between_dates	Ensures date is within a range
recent_date	Ensures date is within N days
date_diff_less_than	Ensures difference between two dates is within threshold
no_weekend_dates	Ensures dates do not fall on weekends

Example

columns:
  - name: date
    data_tests:
      - dbt_checks.recent_date:
          arguments:
            max_age_days: 7

Aggregation

Aggregation checks validate dataset-level metrics.

Check	Description
row_count_greater_than	Ensures model has at least N rows
row_count_less_than	Ensures model has at most N rows
row_count_between	Ensures row count falls within range
sum_between	Ensures column sum falls within range
avg_between	Ensures column average falls within range
max_between	Ensures column maximum falls within range
min_between	Ensures column minimum falls within range

Example

models:
  - name: orders
    data_tests:
      - dbt_checks.row_count_greater_than:
          arguments:
            min_value: 100

Ratio

Ratio checks validate proportions of rows matching a condition.

Check	Description
null_ratio_below	Ensures null ratio is below threshold
null_ratio_between	Ensures null ratio is within range
positive_ratio_between	Ensures positive value ratio within range
negative_ratio_between	Ensures negative value ratio within range
value_ratio_between	Ensures specific value ratio within range

Example

columns:
  - name: email
    data_tests:
      - dbt_checks.null_ratio_below:
          arguments:
            threshold: 0.05

Supported Warehouses

dbt-checks is designed to work across common dbt adapters:

• Snowflake
• BigQuery
• Databricks
• Spark
• Redshift
• Postgres

Adapter-specific behavior is handled through dbt's dispatch mechanism.

Tested on DuckDB in CI.

Aditional adapters are supported through dbt dispatch (best-efort compatibility).

Why dbt-checks?

Many dbt projects repeatedly implement the same validation logic.

dbt-checks provides:

• reusable checks
• simple configuration
• consistent validation patterns
• cross-warehouse compatibility

Contributing

Contributions are welcome.

To add a new check:

1. Implement it in macros/tests
2. Reuse helper macros when possible
3. Add documentation
4. Add integration tests

License

This project is licensed under the MIT License — see the LICENSE file for details.