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Related documentation#

Getting started#

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

Declaring an exposure#

Exposures are defined in .yml files in your models directory (as defined by the source-paths config), nested under an exposures: key.

exposures:    - name: weekly_jaffle_metrics    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')          owner:      name: Claire from Data      email:

Available properties#


  • name (must be unique among exposures)
  • type: one of dashboard, notebook, analysis, ml, application (used to organize in docs site)
  • owner: email


  • depends_on: list of refable nodes (ref + source)


  • url
  • maturity: one of high, medium, low
  • owner: name

General properties (optional)

  • description
  • tags
  • meta

We plan to add more subtypes and optional properties in future releases.

Referencing exposures#

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

dbt run -s +exposure:weekly_jaffle_metricsdbt test -s +exposure:weekly_jaffle_metrics

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

Dedicated page in dbt-docs for each exposure

Dedicated page in dbt-docs for each exposure

Exposures appear as orange-y nodes in the DAG

Exposures appear as orange-y nodes in the DAG

Exposures are new!#

Exposures were introduced in dbt v0.18.1, with a limited set of supported types and properties. If you're interested in requesting or contributing additional properties, check out issue dbt#2835.