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: data@jaffleshop.com

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: data@jaffleshop.com

    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: data@jaffleshop.com
  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: data@jaffleshop.com
  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.

JSON schemaPython tests