Add Exposures to your DAG

Exposures make it possible to define and describe a downstream use of your dbt project, such as in a dashboard, application, or data science pipeline. By defining exposures, you can then:

• run, test, and list resources that feed into your exposure
• populate a dedicated page in the auto-generated documentation site with context relevant to data consumers

Exposures can be defined in two ways:

• Manual — Declared explicitly in your project’s YAML files.
• Automatic — dbt Cloud creates and visualizes downstream exposures automatically for supported integrations, removing the need for manual YAML definitions. These downstream exposures are stored in dbt’s metadata system, appear in Explorer, and behave like manual exposures. However, they don’t exist in YAML files.

Declaring an exposure

Exposures are defined in .yml files nested under an exposures: key.

The following example shows an exposure definition in a models/<filename>.yml file:

models/<filename>.yml

version: 2

exposures:

  - name: weekly_jaffle_metrics
    label: Jaffles by the Week
    type: dashboard
    maturity: high
    url: https://bi.tool/dashboards/1
    description: >
      Did someone say "exponential growth"?

    depends_on:
      - ref('fct_orders')
      - ref('dim_customers')
      - source('gsheets', 'goals')
      - metric('count_orders')

    owner:
      name: Callum McData
      email: data@jaffleshop.com

Available properties

Required:

• name: a unique exposure name written in snake case
• type: one of dashboard, notebook, analysis, ml, application (used to organize in docs site)
• owner: name or email required; additional properties allowed

Expected:

• depends_on: list of refable nodes, including metric, ref, and source. While possible, it is highly unlikely you will ever need an exposure to depend on a source directly.

Optional:

• label: May contain spaces, capital letters, or special characters.
• url: Activates and populates the link to View this exposure in the upper right corner of the generated documentation site
• maturity: Indicates the level of confidence or stability in the exposure. One of high, medium, or low. For example, you could use high maturity for a well-established dashboard, widely used and trusted within your organization. Use low maturity for a new or experimental analysis.

General properties (optional)

• description
• tags
• meta
• enabled — You can set this property at the exposure level or at the project level in the dbt_project.yml file.

Referencing exposures

Once an exposure is defined, you can run commands that reference it:

dbt run -s +exposure:weekly_jaffle_report
dbt test -s +exposure:weekly_jaffle_report

When we generate the Explorer site, you'll see the exposure appear:

Exposures has a dedicated section, under the 'Resources' tab in dbt Explorer, which lists each exposure in your project.

Exposures appear as nodes in the dbt Explorer DAG. It displays an orange 'EXP' indicator within the node.

Related docs

• Exposure properties
• exposure: selection method
• Data health tiles