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Concepts
The three dbt project layers
Staging, intermediate, marts — what each layer is for, why you should resist inventing a fourth, and how it maps to the medallion architecture on Databricks.
Materializations: when to use which
dbt-databricks supports five materializations. This is the decision framework for picking the right one per model.
How-tos
Build your first incremental dbt model
Moving a model from full refresh to incremental without breaking downstream consumers. Step-by-step with Databricks-specific choices.
Set up Slim CI for your dbt project
Running only modified models and their downstream, deferring everything else to prod. The pattern that keeps CI under five minutes as the project grows.
Orchestrate dbt with Airflow + Astronomer Cosmos
One Airflow task per dbt model, data-aware scheduling, and how to avoid the single-BashOperator trap.
Triage a failed dbt model
The first five minutes after a dbt run fails: classify the error, identify the blast radius, and pick the right recovery path.
CLI reference
Standards
dbt model authoring standards
The Causeway rules for how a dbt model should look. Naming, structure, materialization, testing, and contracts. Enforced at review time.
dbt production readiness checklist
What a Causeway dbt project must satisfy before the first prod deploy, and what it must keep satisfying after. Enforced at the Gold-promotion gate.
Reference
Materialization options reference
The full config matrix for every dbt-databricks materialization, including incremental strategies, clustering, compute routing, and schema-change handling.
Common dbt errors and resolutions
Symptom-first lookup for the errors you hit weekly: compilation, database, incremental schema drift, permissions, package conflicts.