Features
dbt commands are CLI instructions that let engineers build, test, document, and deploy data transformations using Data Build Tool (dbt).
dbt commands are terminal instructions that trigger dbt s core functions: building models, running tests, generating docs, and deploying code. They orchestrate SQL transformations as code, enabling version control and automation.
Engineers use dbt commands to convert raw warehouse tables into reliable, tested models quickly. Commands automate SQL compilation, dependency resolution, and documentation to enforce analytics engineering best practices.
Run dbt init my_project to scaffold directories, profiles, and example model files. The command sets up a ready to run dbt workspace with sample YAML configs.
Execute dbt run to compile Jinja templated SQL, resolve dependencies, and run statements in the target warehouse. Use selectors (dbt run -s tag:nightly) for granular execution.
Use dbt test to launch schema and data tests defined in YAML or SQL. dbt halts on failures, ensuring model quality before promotion.
Generate static documentation via dbt docs generate. Serve it locally with dbt docs serve to explore lineage graphs and column descriptions in a browser.
Invoke dbt run --select state:modified+ with the --state flag pointing at the last production manifest to rebuild only changed or downstream models, saving compute.
dbt compile renders Jinja templates to raw SQL without execution. Teams use it for CI pipelines to validate syntax and catch errors before warehouse runs.
Galaxy s SQL editor detects dbt project files. You can author .sql and .yml files, then trigger dbt run or dbt test from an integrated terminal. Query results and model lineage display side by side, leveraging Galaxy s AI copilot for auto completion.
Pin dbt version in packages.yml, run dbt deps in CI, adopt dbt seed for lookup tables, and use --warn-error to fail builds on warnings. Schedule dbt source freshness nightly.
Slow builds: use late binding views or incremental models. Flaky tests: assert non null and uniqueness on primary keys. Missing documentation: add dbt docs to CI to enforce docs coverage.
Mastering dbt commands accelerates data pipeline development. Engineers can version SQL, enforce testing, and deploy reliably, reducing manual work and runtime errors. dbt first workflows promote analytics engineering discipline, yielding trusted, maintainable transformations.
Use GitHub Actions or GitLab CI to run dbt deps, compile, run, and test against staging. Promote to production on success.
Run dbt ls --output graph to output model lineage in JSON, useful for orchestration tools.
Yes. Open Galaxy s integrated terminal, navigate to your dbt project, and issue dbt run or dbt test. Galaxy s AI copilot assists with command flags.
Edit packages.yml, then run dbt deps to install or update dependencies like dbt_utils.
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