Elementary dbt package includes exposure validation tests, implemented as dbt tests. These tests can detect changes in your models’ columns that break downstream exposures, such as BI dashboards.

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Configure your elementary exposure validation tests

Within your models directory, add a file called exposures.yml 
version: 2
exposures:
  - name: customers
    label: Customer Dashboard
    type: dashboard
    maturity: high
    url: https://your.bi.tool/dashboards/1
    description: >
      Shows customer growth

    depends_on:
      - ref('customers')

    owner:
      name: Callum McData
      email: [email protected]
You can read up about exposures and how to add them at dbt documentation

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Adding the Elementary exposure information

For each exposure you wish to verify, add a new property called meta: 
version: 2
exposures:
  - name: customers
    label: Customer Dashboard
    type: dashboard
    maturity: high
    url: https://your.bi.tool/dashboards/1
    description: >
      Shows customer growth

    depends_on:
      - ref('customers')

    owner:
      name: Callum McData
      email: [email protected]

    meta:
      referenced_columns:
        - column_name: "customer_id"
In Elementary Cloud, the exposures and exposure validation tests are added automatically, leveraging an integration with your BI tool.
You can optionally also specify the column data type (data_type) 
version: 2
- name: returned_orders
  label: Returned Orders
  type: dashboard
  maturity: high
  url: https://your.bi.tool/dashboards/2
  description: >
    Returned orders over time

  depends_on:
    - ref('returned_orders')

  owner:
    name: Callum McData
    email: [email protected]
  meta:
    referenced_columns:
      - column_name: "order_id"
        data_type: "numeric"
In addition, if your exposure depends on several nodes, you can depend explicitly per column which source should be tested: 
version: 2
- name: returned_orders
  label: Returned Orders
  type: dashboard
  maturity: high
  url: https://your.bi.tool/dashboards/2
  description: >
    Returned orders over time

  depends_on:
    - ref('returned_orders')
    - ref('orders')

  owner:
    name: Callum McData
    email: [email protected]
  meta:
    referenced_columns:
      - column_name: "order_id"
        data_type: "numeric"
        node: ref('returned_orders')
      - column_name: "customer_id"
        data_type: "numeric"
        node: ref('orders')

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Adding the Elementary exposure tests for your module

For each module schema you wish to verify the exposure dependencies, add the elementary exposure tests: 
  ...
  - name: returned_orders
    description: This table contains all of the returned orders
    config:
      tags: ["finance"]

    tests:
      - elementary.volume_anomalies:
          tags: ["table_anomalies"]
          timestamp_column: "order_date"
      - elementary.exposure_schema_validity:
          tags: [elementary]
We recommend adding a tag to the tests so you can execute these in a dedicated run using the selection parameter --select tag:elementary. Upon running the tests, if breaking changes are detected in the model, the test will fail and in the test result query you’ll be able to see the reasons: 
SELECT 'customers' as exposure, 'https://your.bi.tool/dashboards/1' as url, 'different data type for the column customer_id numeric vs numeric' as error
UNION ALL SELECT 'customers' as exposure, 'https://your.bi.tool/dashboards/1' as url, 'order_id column missing in the model' as error

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What does it mean when a test fails?

When a test fails, it means that an exposure is potentially broken due to a missing or wrongly-typed column. Open your BI tool to validate, and if you make any changes to your dashboards be sure to update the exposures.yml and your model schema accordingly.