Do you normally deploy dbt code/models from one environment to another? For example, working in a dev instance of Databricks and then moving to production. How do you manage environment variables, connections, and passwords to keep them secure and easy to manage?

Deploying dbt models across environments typically follows a three‑tier approach: dev, staging, and prod. In Databricks, each environment has its own cluster, database, and secrets.

1. Profiles and environment variables: dbt uses a profiles.yml file to define connection parameters. Instead of hard‑coding values, we reference Databricks secrets using the {{ env_var('VAR_NAME') }} syntax. Each environment has its own set of secrets stored in Databricks Secret Scope.

1. Secrets management: Store credentials (token, password, JDBC URL) in Databricks Secret Scopes. In the CI/CD pipeline, inject the appropriate secret scope name via an environment variable. dbt will resolve {{ env_var('DBT_SECRET_SCOPE') }} to the correct scope.

1. CI/CD integration: Use a tool like GitHub Actions, GitLab CI, or Azure Pipelines. The pipeline checks out the repo, sets the DBT_PROFILE environment variable to point to the target environment, and runs dbt run --target <env>. The --target flag selects the profile section.

1. Versioned deployments: Tag releases in Git. The pipeline pulls the tag, runs dbt test to validate, then executes dbt run. If the run succeeds, the models are materialized in the target environment.

1. Rollback strategy: Keep a history of dbt snapshots or use Databricks Delta tables’ time travel to revert to a previous state.

By externalizing secrets, using environment‑specific profiles, and automating the flow with CI/CD, we keep credentials secure, avoid duplication, and ensure consistent deployments across dev, staging, and prod.