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dbt tips and tricks

Use this page for valuable insights and practical advice to enhance your dbt experience. Whether you're new to dbt or an experienced user, these tips are designed to help you work more efficiently and effectively.

The following tips are organized into the following categories:

If you're developing with the dbt Cloud IDE, you can refer to the keyboard shortcuts page to help make development more productive and easier for everyone.

Package tips

Leverage these dbt packages to streamline your workflow:

dbt_codegenUse the package to help you generate YML files for your models and sources and SQL files for your staging models.
dbt_utilsThe package contains macros useful for daily development. For example, date_spine generates a table with all dates between the ones provided as parameters.
dbt_project_evaluatorThe package compares your dbt project against a list of our best practices and provides suggestions and guidelines on how to update your models.
dbt_expectationsThe package contains many tests beyond those built into dbt.
dbt_audit_helperThe package lets you compare the output of 2 queries. Use it when refactoring existing logic to ensure that the new results are identical.
dbt_artifactsThe package saves information about your dbt runs directly to your data platform so that you can track the performance of models over time.
dbt_meta_testingThis package checks that your dbt project is sufficiently tested and documented.

Advanced tips and techniques

  • Use your folder structure as your primary selector method. dbt build --select is simpler and more resilient than relying on tagging every model.
  • Think about jobs in terms of build cadences and SLAs. Run models that have hourly, daily, or weekly build cadences together.
  • Use the where config for tests to test an assertion on a subset of records.
  • store_failures lets you examine records that cause tests to fail, so you can either repair the data or change the test as needed.
  • Use severity thresholds to set an acceptable number of failures for a test.
  • Use incremental_strategy in your incremental model config to implement the most effective behavior depending on the volume of your data and reliability of your unique keys.
  • Set vars in your dbt_project.yml to define global defaults for certain conditions, which you can then override using the --vars flag in your commands.
  • Use for loops in Jinja to DRY up repetitive logic, such as selecting a series of columns that all require the same transformations and naming patterns to be applied.
  • Instead of relying on post-hooks, use the grants config to apply permission grants in the warehouse resiliently.
  • Define source-freshness thresholds on your sources to avoid running transformations on data that has already been processed.
  • Use the + operator on the left of a model dbt build --select +model_name to run a model and all of its upstream dependencies. Use the + operator on the right of the model dbt build --select model_name+ to run a model and everything downstream that depends on it.
  • Use dir_name to run all models in a package or directory.
  • Use the @ operator on the left of a model in a non-state-aware CI setup to test it. This operator runs all of a selection’s parents and children, and also runs the parents of its children, which in a fresh CI schema will likely not exist yet.
  • Use the --exclude flag to remove a subset of models out of a selection.
  • Use the --full-refresh flag to rebuild an incremental model from scratch.
  • Use seeds to create manual lookup tables, like zip codes to states or marketing UTMs to campaigns. dbt seed will build these from CSVs into your warehouse and make them ref able in your models.
  • Use to pivot logic based on what environment you’re using. For example, to build into a single development schema while developing, but use multiple schemas in production.