Manifest Checks: Seeds

Note

The below checks require manifest.json to be present.

Functions:

Name	Description
check_seed_column_names	Seed columns must have names that match the supplied regex.
check_seed_columns_have_types	Columns defined for seeds must have a data_type declared.
check_seed_description_populated	Seeds must have a populated description.
check_seed_has_unit_tests	Seeds must have more than the specified number of unit tests.
check_seed_names	Seed must have a name that matches the supplied regex.

check_seed_column_names

Seed columns must have names that match the supplied regex.

Rationale

Seeds are loaded directly into the warehouse from CSV files, and their column names become part of the project's data contract. Inconsistent casing or naming conventions (e.g. mixing camelCase and snake_case) causes friction when joining seed data with other models and makes the project harder to maintain. Enforcing a naming pattern keeps seed columns consistent with the rest of the project.

Parameters:

Name	Type	Description	Default
seed_column_name_pattern	str	Regexp the column name must match.	required

Receives at execution time:

Name	Type	Description
seed	SeedNode	The SeedNode object to check.

Other Parameters (passed via config file):

Name	Type	Description
description	str | None	Description of what the check does and why it is implemented.
exclude	str | None	Regex pattern to match the seed path. Seed paths that match the pattern will not be checked.
include	str | None	Regex pattern to match the seed path. Only seed paths that match the pattern will be checked.
severity	Literal[error, warn] | None	Severity level of the check. Default: error.

Example(s):

manifest_checks:
    - name: check_seed_column_names
      seed_column_name_pattern: ^[a-z_]+$  # Lowercase with underscores only

Source code in src/dbt_bouncer/checks/manifest/check_seeds.py

@check
def check_seed_column_names(seed, *, seed_column_name_pattern: str):
    """Seed columns must have names that match the supplied regex.

    !!! info "Rationale"

        Seeds are loaded directly into the warehouse from CSV files, and their column names become part of the project's data contract. Inconsistent casing or naming conventions (e.g. mixing camelCase and snake_case) causes friction when joining seed data with other models and makes the project harder to maintain. Enforcing a naming pattern keeps seed columns consistent with the rest of the project.

    Parameters:
        seed_column_name_pattern (str): Regexp the column name must match.

    Receives:
        seed (SeedNode): The SeedNode object to check.

    Other Parameters:
        description (str | None): Description of what the check does and why it is implemented.
        exclude (str | None): Regex pattern to match the seed path. Seed paths that match the pattern will not be checked.
        include (str | None): Regex pattern to match the seed path. Only seed paths that match the pattern will be checked.
        severity (Literal["error", "warn"] | None): Severity level of the check. Default: `error`.

    Example(s):
        ```yaml
        manifest_checks:
            - name: check_seed_column_names
              seed_column_name_pattern: ^[a-z_]+$  # Lowercase with underscores only
        ```

    """
    compiled_pattern = compile_pattern(seed_column_name_pattern.strip())
    seed_columns = seed.columns or {}
    non_complying_columns = [
        col_name
        for col_name in seed_columns
        if compiled_pattern.match(str(col_name)) is None
    ]

    if non_complying_columns:
        fail(
            f"`{get_clean_model_name(seed.unique_id)}` has columns that do not match the supplied regex `{seed_column_name_pattern.strip()}`: {non_complying_columns}"
        )

check_seed_columns_have_types

Columns defined for seeds must have a data_type declared.

Rationale

Without explicit data_type declarations, dbt seeds rely on the warehouse to infer column types from the CSV data, which can lead to unexpected type coercions (e.g. numeric IDs loaded as strings, dates loaded as text). Declaring types explicitly ensures the seed loads with the expected schema and prevents subtle data quality issues downstream.

Receives at execution time:

Name	Type	Description
seed	SeedNode	The SeedNode object to check.

Other Parameters (passed via config file):

Name	Type	Description
description	str | None	Description of what the check does and why it is implemented.
exclude	str | None	Regex pattern to match the seed path. Seed paths that match the pattern will not be checked.
include	str | None	Regex pattern to match the seed path. Only seed paths that match the pattern will be checked.
severity	Literal[error, warn] | None	Severity level of the check. Default: error.

Example(s):

manifest_checks:
    - name: check_seed_columns_have_types

Source code in src/dbt_bouncer/checks/manifest/check_seeds.py

@check
def check_seed_columns_have_types(seed):
    """Columns defined for seeds must have a `data_type` declared.

    !!! info "Rationale"

        Without explicit `data_type` declarations, dbt seeds rely on the warehouse to infer column types from the CSV data, which can lead to unexpected type coercions (e.g. numeric IDs loaded as strings, dates loaded as text). Declaring types explicitly ensures the seed loads with the expected schema and prevents subtle data quality issues downstream.

    Receives:
        seed (SeedNode): The SeedNode object to check.

    Other Parameters:
        description (str | None): Description of what the check does and why it is implemented.
        exclude (str | None): Regex pattern to match the seed path. Seed paths that match the pattern will not be checked.
        include (str | None): Regex pattern to match the seed path. Only seed paths that match the pattern will be checked.
        severity (Literal["error", "warn"] | None): Severity level of the check. Default: `error`.

    Example(s):
        ```yaml
        manifest_checks:
            - name: check_seed_columns_have_types
        ```

    """
    columns = seed.columns or {}
    untyped_columns = [
        col_name for col_name, col in columns.items() if not col.data_type
    ]
    if untyped_columns:
        fail(
            f"`{get_clean_model_name(seed.unique_id)}` has columns without a declared `data_type`: {untyped_columns}"
        )

check_seed_description_populated

Seeds must have a populated description.

Rationale

Seeds represent static reference or lookup data that is loaded into the warehouse from version-controlled CSV files. Without descriptions, it is unclear what a seed contains, why it exists, and when it was last validated — making it difficult for new team members to assess whether data they find in the warehouse is the right dataset to use.

Parameters:

Name	Type	Description	Default
min_description_length	int | None	Minimum length required for the description to be considered populated.	None

Receives at execution time:

Name	Type	Description
seed	SeedNode	The SeedNode object to check.

Other Parameters (passed via config file):

Name	Type	Description
description	str | None	Description of what the check does and why it is implemented.
exclude	str | None	Regex pattern to match the seed path. Seed paths that match the pattern will not be checked.
include	str | None	Regex pattern to match the seed path. Only seed paths that match the pattern will be checked.
severity	Literal[error, warn] | None	Severity level of the check. Default: error.

Example(s):

manifest_checks:
    - name: check_seed_description_populated

manifest_checks:
    - name: check_seed_description_populated
      min_description_length: 25 # Setting a stricter requirement for description length

Source code in src/dbt_bouncer/checks/manifest/check_seeds.py

@check
def check_seed_description_populated(
    seed, *, min_description_length: Annotated[int, Field(gt=0)] | None = None
):
    """Seeds must have a populated description.

    !!! info "Rationale"

        Seeds represent static reference or lookup data that is loaded into the warehouse from version-controlled CSV files. Without descriptions, it is unclear what a seed contains, why it exists, and when it was last validated — making it difficult for new team members to assess whether data they find in the warehouse is the right dataset to use.

    Parameters:
        min_description_length (int | None): Minimum length required for the description to be considered populated.

    Receives:
        seed (SeedNode): The SeedNode object to check.

    Other Parameters:
        description (str | None): Description of what the check does and why it is implemented.
        exclude (str | None): Regex pattern to match the seed path. Seed paths that match the pattern will not be checked.
        include (str | None): Regex pattern to match the seed path. Only seed paths that match the pattern will be checked.
        severity (Literal["error", "warn"] | None): Severity level of the check. Default: `error`.

    Example(s):
        ```yaml
        manifest_checks:
            - name: check_seed_description_populated
        ```
        ```yaml
        manifest_checks:
            - name: check_seed_description_populated
              min_description_length: 25 # Setting a stricter requirement for description length
        ```

    """
    if not is_description_populated(
        seed.description or "", min_description_length or 4
    ):
        fail(
            f"`{get_clean_model_name(seed.unique_id)}` does not have a populated description."
        )

check_seed_has_unit_tests

Seeds must have more than the specified number of unit tests.

Rationale

Seeds contain static data that is loaded directly into the warehouse, and their transformations (via the ref() function in downstream models) need to be verifiable. Unit tests on seeds validate that the downstream logic correctly handles the reference data — catching issues like missing mappings, unexpected nulls, or edge-case values before they reach production.

Parameters:

Name	Type	Description	Default
min_number_of_unit_tests	int | None	The minimum number of unit tests that a seed must have.	1

Receives at execution time:

Name	Type	Description
manifest_obj	ManifestObject	The ManifestObject object parsed from manifest.json.
seed	SeedNode	The SeedNode object to check.
unit_tests	list[UnitTests]	List of UnitTests objects parsed from manifest.json.

Other Parameters (passed via config file):

Name	Type	Description
description	str | None	Description of what the check does and why it is implemented.
exclude	str | None	Regex pattern to match the seed path. Seed paths that match the pattern will not be checked.
include	str | None	Regex pattern to match the seed path. Only seed paths that match the pattern will be checked.
severity	Literal[error, warn] | None	Severity level of the check. Default: error.

Warning

This check is only supported for dbt 1.8.0 and above.

Example(s):

manifest_checks:
    - name: check_seed_has_unit_tests
      include: ^seeds/core

manifest_checks:
    - name: check_seed_has_unit_tests
      min_number_of_unit_tests: 2

Source code in src/dbt_bouncer/checks/manifest/check_seeds.py

@check
def check_seed_has_unit_tests(
    seed, ctx, *, min_number_of_unit_tests: Annotated[int, Field(gt=0)] = 1
):
    """Seeds must have more than the specified number of unit tests.

    !!! info "Rationale"

        Seeds contain static data that is loaded directly into the warehouse, and their transformations (via the `ref()` function in downstream models) need to be verifiable. Unit tests on seeds validate that the downstream logic correctly handles the reference data — catching issues like missing mappings, unexpected nulls, or edge-case values before they reach production.

    Parameters:
        min_number_of_unit_tests (int | None): The minimum number of unit tests that a seed must have.

    Receives:
        manifest_obj (ManifestObject): The ManifestObject object parsed from `manifest.json`.
        seed (SeedNode): The SeedNode object to check.
        unit_tests (list[UnitTests]): List of UnitTests objects parsed from `manifest.json`.

    Other Parameters:
        description (str | None): Description of what the check does and why it is implemented.
        exclude (str | None): Regex pattern to match the seed path. Seed paths that match the pattern will not be checked.
        include (str | None): Regex pattern to match the seed path. Only seed paths that match the pattern will be checked.
        severity (Literal["error", "warn"] | None): Severity level of the check. Default: `error`.

    !!! warning

        This check is only supported for dbt 1.8.0 and above.

    Example(s):
        ```yaml
        manifest_checks:
            - name: check_seed_has_unit_tests
              include: ^seeds/core
        ```
        ```yaml
        manifest_checks:
            - name: check_seed_has_unit_tests
              min_number_of_unit_tests: 2
        ```

    """
    manifest_obj = ctx.manifest_obj
    if get_package_version_number(
        manifest_obj.manifest.metadata.dbt_version or "0.0.0"
    ) >= get_package_version_number("1.8.0"):
        num_unit_tests = len(
            [
                t.unique_id
                for t in ctx.unit_tests
                if t.depends_on
                and t.depends_on.nodes
                and t.depends_on.nodes[0] == seed.unique_id
            ]
        )
        if num_unit_tests < min_number_of_unit_tests:
            fail(
                f"`{get_clean_model_name(seed.unique_id)}` has {num_unit_tests} unit tests, this is less than the minimum of {min_number_of_unit_tests}."
            )
    else:
        logging.warning(
            "This unit test check is only supported for dbt 1.8.0 and above."
        )

check_seed_names

Seed must have a name that matches the supplied regex.

Rationale

Seed naming conventions help analysts and engineers quickly identify reference data in the warehouse and distinguish seeds from models or sources. A consistent prefix or pattern (e.g. ref_ or raw_) makes it clear what type of data an object contains and prevents naming collisions between seeds and other resources in the project.

Parameters:

Name	Type	Description	Default
seed_name_pattern	str	Regexp the seed name must match.	required

Receives at execution time:

Name	Type	Description
seed	SeedNode	The SeedNode object to check.

Other Parameters (passed via config file):

Name	Type	Description
description	str | None	Description of what the check does and why it is implemented.
exclude	str | None	Regex pattern to match the seed path. Seed paths that match the pattern will not be checked.
include	str | None	Regex pattern to match the seed path. Only seed paths that match the pattern will be checked.
severity	Literal[error, warn] | None	Severity level of the check. Default: error.

Example(s):

manifest_checks:
    - name: check_seed_names
      include: ^seeds
      seed_name_pattern: ^raw_

Source code in src/dbt_bouncer/checks/manifest/check_seeds.py

@check
def check_seed_names(seed, *, seed_name_pattern: str):
    """Seed must have a name that matches the supplied regex.

    !!! info "Rationale"

        Seed naming conventions help analysts and engineers quickly identify reference data in the warehouse and distinguish seeds from models or sources. A consistent prefix or pattern (e.g. `ref_` or `raw_`) makes it clear what type of data an object contains and prevents naming collisions between seeds and other resources in the project.

    Parameters:
        seed_name_pattern (str): Regexp the seed name must match.

    Receives:
        seed (SeedNode): The SeedNode object to check.

    Other Parameters:
        description (str | None): Description of what the check does and why it is implemented.
        exclude (str | None): Regex pattern to match the seed path. Seed paths that match the pattern will not be checked.
        include (str | None): Regex pattern to match the seed path. Only seed paths that match the pattern will be checked.
        severity (Literal["error", "warn"] | None): Severity level of the check. Default: `error`.

    Example(s):
        ```yaml
        manifest_checks:
            - name: check_seed_names
              include: ^seeds
              seed_name_pattern: ^raw_
        ```

    """
    compiled_pattern = compile_pattern(seed_name_pattern.strip())
    if compiled_pattern.match(str(seed.name)) is None:
        fail(
            f"`{get_clean_model_name(seed.unique_id)}` does not match the supplied regex `{seed_name_pattern.strip()}`."
        )