dbt-expert

dbt debug checks your connection configuration and profiles directory setup. Run dbt debug with your profiles directory specified to verify credentials, database access, and project configuration before executing transforms, catching connection issues early.

dbt best practices include organizing models across staging, intermediate, and marts layers, using consistent naming conventions, and executing commands with standardized options like --profiles-dir and --no-use-colors. This ensures reproducible builds and maintainable analytics engineering workflows.

Create isolated issue-specific targets in dbt to prevent production impacts when testing. Use target management to switch between environments, ensuring development and staging work stays separate from production data pipelines.

Yes, dbt supports Databricks SQL with dialect-specific guidance for command execution and model referencing. The dbt-expert skill provides Databricks-tailored procedures for compile, run, show, and test commands.

Core dbt commands include compile (parse project structure), run (execute models), test (validate data quality), and show (preview query results). These commands form the foundation of model development, validation, and ad-hoc exploration in data transformation pipelines.

Apply documentation and style guidelines to model definitions, references, and configuration files. Consistent project structure across staging, intermediate, and marts layers enables team collaboration and reduces maintenance overhead in analytics engineering projects.