meta

Models

dbt_project.yml

models:
  <resource-path>:
    +meta: {<dictionary>}

models/schema.yml

models:
  - name: model_name
    config:
      meta: {<dictionary>}

    columns:
      - name: column_name
        config:
          meta: {<dictionary>} # changed to config in v1.10 and backported to 1.9

The meta config can be defined:

• Under the models config in the project file (shown in previous 'models/schema.yml' example)
• Under the models config in the project file (dbt_project.yml)
• in a config() Jinja macro within a model's SQL file

See configs and properties for details.

Sources

dbt_project.yml

sources:
  <resource-path>:
    +meta: {<dictionary>}

models/schema.yml

sources:
  - name: model_name
    config:
      meta: {<dictionary>}

    tables:
      - name: table_name
        config:
          meta: {<dictionary>}

        columns:
          - name: column_name
            config:
              meta: {<dictionary>} # changed to config in v1.10 and backported to 1.9

Seeds

dbt_project.yml

seeds:
  <resource-path>:
    +meta: {<dictionary>}

seeds/schema.yml

seeds:
  - name: seed_name
    config:
      meta: {<dictionary>}

    columns:
      - name: column_name
        config:
          meta: {<dictionary>} # changed to config in v1.10 and backported to 1.9

The meta config can be defined:

• Under the seeds config in the property file (shown in in previous 'seeds/schema.yml' example)
• Under the seeds config in the project file (dbt_project.yml). See configs and properties for details.

Snapshots

dbt_project.yml

snapshots:
  <resource-path>:
    +meta: {<dictionary>}

snapshots/schema.yml

snapshots:
  - name: snapshot_name
    config:
      meta: {<dictionary>}

    columns:
      - name: column_name
        config:
          meta: {<dictionary>} # changed to config in v1.10 and backported to 1.9

The meta config can be defined:

• under the snapshots config in the properties file (shown in previous snapshots/schema.yml example)
• under the snapshots config in the project file (dbt_project.yml)
• in a config() Jinja macro within a snapshot's SQL block

See configs and properties for details.

Tests

Use the meta field to add metadata to generic or singular tests. meta accepts key-value pairs, is compiled into manifest.json, and appears in auto-generated documentation.

Generic data tests

Add meta under the config block in your properties.yml file:

models/properties.yml

models:
  - name: my_model
    columns:
      - name: my_column
        data_tests:
          - unique:
              config:
                meta:
                  owner: "docs team"

Or set defaults in dbt_project.yml:

dbt_project.yml

data_tests:
  my_project:
    +meta:
      owner: "docs team"

Singular data tests

Add meta in the SQL test file using config():

tests/my_singular_test.sql

{{ config(meta={'owner': 'docs team'}) }}

select * from {{ ref('my_model') }}
where my_column is null

Or document in tests/properties.yml:

tests/properties.yml

data_tests:
  - name: my_singular_test
    config:
      meta:
        owner: "analytics_team"

Unit tests

💡Did you know...

Available from dbt v1.8 or with the dbt "Latest" release track.

dbt_project.yml

unit_tests:
  <resource-path>:
    +meta: {<dictionary>}

models/<filename>.yml

unit_tests:
  - name: <test-name>
    config:
      meta: {<dictionary>}

Analyses

The meta config is not currently supported for analyses.

Macros

dbt_project.yml

macros:
  <resource-path>:
    +meta: {<dictionary>}

macros/schema.yml

macros:
  - name: macro_name
    config:
      meta: {<dictionary>} # changed to config in v1.10

    arguments:
      - name: argument_name

Exposures

dbt_project.yml

exposures:
  <resource-path>:
    +meta: {<dictionary>}

models/exposures.yml

exposures:
  - name: exposure_name
    config:
      meta: {<dictionary>} # changed to config in v1.10

Semantic models

The meta config can be defined:

• Under the semantic-models config in the properties file (as showin in previous models/semantic_models.yml example)
• Under the semantic-models config in the project file (dbt_project.yml). See configs and properties for details.

Metrics

dbt_project.yml

metrics:
  <resource-path>:
    +meta: {<dictionary>}

Saved queries

dbt_project.yml

saved-queries:
  <resource-path>:
    +meta: {<dictionary>}

models/semantic_models.yml

saved_queries:
  - name: saved_query_name
    config:
      meta: {<dictionary>}

Definition

The meta field can be used to set metadata for a resource and accepts any key-value pairs. This metadata is compiled into the manifest.json file generated by dbt, and is viewable in the auto-generated documentation.

Depending on the resource you're configuring, meta may be available within the config property, and/or as a top-level key. (For backwards compatibility, meta is often (but not always) supported as a top-level key, though without the capabilities of config inheritance.)

Examples

To demonstrate how to use the meta config, here are some examples:

• Designate a model owner
• Designate a source column as containing PII
• Configure one meta attribute for all seeds
• Override one meta attribute for a single model
• Assign owner and favorite_color in the dbt_project.yml as a config property
• Assign meta to semantic model
• Assign meta to dimensions, measures, entities
• Add meta to generic and singular data tests
• Access meta values in Python models

Designate a model owner

Additionally, indicate the maturity of a model using a model_maturity: key.

models/schema.yml

models:
  - name: users
    config:
      meta:
        owner: "@alice"
        model_maturity: in dev

Designate a source column as containing PII

models/schema.yml

sources:
  - name: salesforce
    tables:
      - name: account
        config:
          meta:
            contains_pii: true
        columns:
          - name: email
            config:
              meta: # changed to config in v1.10 and backported to 1.9
                contains_pii: true

Configure one meta attribute for all seeds

dbt_project.yml

seeds:
  +meta:
    favorite_color: red

Override one meta attribute for a single model

models/my_model.sql

{{ config(meta = {
    'single_key': 'override'
}) }}

select 1 as id

Assign owner and favorite_color in the dbt_project.yml as a config property

dbt_project.yml

models:
  jaffle_shop:
    +meta:
      owner: "@alice"
      favorite_color: red

Assign meta to semantic model

Assign meta to dimensions, measures, entities

Add meta to generic and singular data tests

The following examples show how to add meta to generic data tests in a properties.yml file, and to singular data tests using config(). You can also set defaults in dbt_project.yml or tests/properties.yml.

Generic data test

models/properties.yml

models:
  - name: orders
    columns:
      - name: order_id
        data_tests:
          - not_null:
              config:
                meta:
                  owner: "@data_team"

Singular data test

tests/assert_order_ids.sql

{{ config(meta={'owner': '@data_team'}) }}

select *
from {{ ref('orders') }}
where order_id is null

Access meta values in Python models

To access custom meta values in Python models, use the dbt.config.meta_get() method.

For example, if you have a model named my_python_model and you want to store custom values, you can do the following:

models/schema.yml

models:
  - name: my_python_model
    config:
      meta:
        batch_size: 1000
        processing_mode: "incremental"

models/my_python_model.py

def model(dbt, session):
    # Access custom values stored in meta directly
    batch_size = dbt.config.meta_get("batch_size")
    processing_mode = dbt.config.meta_get("processing_mode")
    
    # Use the meta values in your model logic
    df = dbt.ref("upstream_model")
    
    if processing_mode == "incremental":
        df = df.limit(batch_size)
    
    return df

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