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meta

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.

Definition​

The meta config sets metadata for a resource and accepts any key-value pairs. This metadata is compiled into the manifest.json file generated by dbt, and is visible 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.)

Effect on state comparison

Changes to meta, including at the column level, don't trigger state:modified. dbt treats meta (and tags) as metadata only, since it doesn't affect how a resource is materialized. Refer to caveats to state comparison for more detail.

Examples​

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

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​

(Applies to dbt v1.12 and later)

The following example shows how to assign a meta value to a semantic model in the model YAML file and dbt_project.yml file:

models:
  - name: fact_transactions
    description: "Transaction fact table at the transaction level. This table contains one row per transaction and includes the transaction timestamp."
    semantic_model:
      enabled: true
      name: transaction
      config:
        meta:
          data_owner: "Finance team"
          used_in_reporting: true

    agg_time_dimension: transaction_date

Assign meta to dimensions, measures, entities​

(Applies to dbt v1.12 and later)

The following example shows how to assign a meta value to a dimension, entity, and simple metrics in a semantic model:

model_name.yml
models:
  - name: model_name
    semantic_model:
      enabled: true
      name: semantic_model

    agg_time_dimension: order_date

    columns:
      - name: order_date
        dimension:
          type: time
          config:
            meta:
              data_owner: "Finance team"
              used_in_reporting: true

      - name: customer_id
        entity:
          type: primary
          config:
            meta:
              description: "Unique identifier for customers"
              data_owner: "Sales team"
              used_in_reporting: false

    metrics:
      - name: count_of_users
        type: simple
        agg: count_distinct
        expr: user_id
        config:
          meta:
            used_in_reporting: true

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.

models/properties.yml
models:
  - name: orders
    columns:
      - name: order_id
        data_tests:
          - not_null:
              config:
                meta:
                  owner: "@data_team"

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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