Features
`dbt run` compiles, executes, and materializes dbt models into your target data warehouse.
dbt run?`dbt run` is the core dbt CLI command that compiles your SQL model files, applies the selected materialization strategy, and issues the resulting SQL against the target warehouse to build or refresh tables and views.
dbt run Work Internally?dbt first parses model dependencies, creates a DAG, jinja-renders SQL with environment variables, and groups models into tasks. It then executes models in topological order, respecting upstream dependencies and concurrency limits.
dbt run?Run after modifying model logic, adding sources, or changing seeds so downstream tables stay in sync. Trigger it in CI/CD, Airflow, or Galaxy’s soon-to-launch job runner to ensure production freshness.
Without flags, dbt builds every model defined in the active project except those disabled via `config(enabled=False)`. Use selectors to narrow scope.
dbt run --select customersCompiles only `models/customers.sql` and its ref-dependencies.
dbt run --select tag:finance+Executes all models tagged finance plus children.
dbt run Flags?--select/--exclude filter nodes; --full-refresh forces incremental models to rebuild; --threads sets concurrency; --state enables defer/run-state workflows for efficient CI.
dbt run?Incremental models detect existing target tables. If present and no `--full-refresh`, dbt inserts only new or updated records defined by the `unique_key` and `incremental_strategy` configs.
dbt runPin warehouse roles and schemas per environment, keep model SQL idempotent, monitor runs with artifacts, and use `dbt test` before promotion. In Galaxy, store endorsed `dbt` query snippets to aid teammates.
dbt run?After a `dbt run`, open Galaxy’s SQL editor to inspect materialized tables, profile column stats, and share validated queries in Collections. The AI copilot can explain generated SQL and suggest optimizations.
dbt run Fail?Check compile errors for Jinja syntax, warehouse permission issues, or missing sources. Review `target/run_results.json`, rerun with `-v`, and validate environment variables.
Limit warehouse credentials, use non-prod schemas for dev runs, and audit run history via dbt Cloud or Warehouse logs. Galaxy’s access controls restrict who can view or alter post-run analyses.
Building data models consistently is foundational. `dbt run` automates model compilation, enforces dependency order, and standardizes deployment across environments. This reduces manual SQL, eliminates stale tables, and accelerates analytics delivery. Teams gain version-controlled, testable transformations that integrate seamlessly with CI/CD and orchestrators like Airflow, Prefect, or upcoming Galaxy workflows.
Yes. Use dbt run --select state:modified+ with a prior manifest to rebuild just edited models and their children.
Duration depends on model count, warehouse resources, and incremental strategy. Parallelize with --threads and prune models with selectors.
You can open the materialized tables in Galaxy, query them with AI assistance, and share endorsed dashboards without leaving the editor.
Yes when paired with tests, role-based access, and CI/CD. Always use proper targets and review run artifacts.
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