database

Model

Specify a custom database for a model in your dbt_project.yml file.

For example, if you have a model that you want to load into a database other than the target database, you can configure it like this:

dbt_project.yml

models:
  your_project:
    sales_metrics:
      +database: reporting

This would result in the generated relation being located in the reporting database, so the full relation name would be reporting.finance.sales_metrics instead of the default target database.

Seeds

Configure a database in your dbt_project.yml file.

For example, to load a seed into a database called staging instead of the target database, you can configure it like this:

dbt_project.yml

seeds:
  your_project:
    product_categories:
      +database: staging

This would result in the generated relation being located in the staging database, so the full relation name would be staging.finance.product_categories.

Snapshots

Tests

Customize the database for storing test results in your dbt_project.yml file.

For example, to save test results in a specific database, you can configure it like this:

dbt_project.yml

tests:
  +store_failures: true
  +database: test_results

This would result in the test results being stored in the test_results database.

Definition

Optionally specify a custom database for a model, seed, snapshot, or data test.

When dbt creates a relation (table/view) in a database, it creates it as: {{ database }}.{{ schema }}.{{ identifier }}, e.g. analytics.finance.payments

The standard behavior of dbt is:

• If a custom database is not specified, the database of the relation is the target database ({{ target.database }}).
• If a custom database is specified, the database of the relation is the {{ database }} value.

To learn more about changing the way that dbt generates a relation's database, read Using Custom Databases

Warehouse specific information

• BigQuery: project and database are interchangeable