dbt build is a single CLI command that runs every selected model, test, snapshot, and seed in dependency order, creating or validating your entire analytics graph in one step.
dbt build is a high-level command that executes all models, tests, snapshots, and seeds that match your selection criteria in a single run. It materializes models, seeds snapshot tables, and applies tests in dependency order, ensuring data freshness and integrity end-to-end.
Prior to v1.3, analysts chained dbt run, dbt test, and dbt snapshot. dbt build merges those steps, cutting command overhead, guaranteeing identical selection logic, and simplifying CI pipelines.
The command first parses manifest.json, resolves dependency graph, then executes tasks in topological order. dbt converts models to SQL, submits them to the warehouse, evaluates test queries, and logs results to the run artifacts.
# run everything
dbt build
# run specific model and downstream dependents
dbt build --select my_model+
# run and test only incremental models
dbt build --select tag:incremental
--select targets nodes, --exclude removes nodes, --full-refresh rebuilds incremental models, --defer allows production references during CI, and --state enables slim incremental builds by comparing manifests.
Add dbt build to your GitHub Actions or GitLab pipeline after install-deps. Limit scope with --select state:modified+ for fast feedback. Fail the job on test errors to block bad code.
Yes. Configure Galaxy’s task runner to call dbt build --project-dir . --profiles-dir ~/.dbt whenever you endorse a production query. Galaxy’s AI copilot autogenerates model SQL and names before the build.
Daily batch rebuilds, pull-request validation, on-demand dataset refresh, and ad-hoc sandbox testing all benefit from dbt build’s one-shot execution.
Tag smoke tests, adopt slim incremental, cache seeds, and monitor run artifacts. Keep builds under 30 minutes; otherwise shard by domain.
dbt build enforces data reliability by guaranteeing models and tests run together. This reduces human error, standardizes CI pipelines, and speeds developer feedback loops. For data engineers, it means fewer moving parts, simpler documentation, and clearer lineage, allowing teams to scale analytics engineering with confidence.
Yes. dbt build combines both commands, plus snapshots and seeds, into a single graph-aware execution.
Galaxy lets you call custom shell commands. Point Galaxy’s task runner to your dbt project and run dbt build after approving a Collection.
Use the --threads flag to match warehouse concurrency and speed up execution. Cluster-aware warehouses like Snowflake auto-scale resources.
Yes, when paired with environments and robust tests. Use defer and state comparison to reference prod models while building in staging.
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