What is the dbt DAG?

The dbt DAG (Directed Acyclic Graph) is the graph of models, sources, seeds, snapshots, and tests connected by dependencies. Nodes are resources; edges come mainly from ref(), source(), and test relationships.

Mental model

source(raw.orders)     source(raw.customers)
        |                       |
        v                       v
   stg_orders              stg_customers
        \                     /
         \                   /
          v                 v
              fct_orders
                  |
                  v
            dim_customer_orders  (example mart)

"Directed" means dependencies have a direction (upstream → downstream). "Acyclic" means no cycles: A cannot ref B if B already depends on A.

How dbt builds it

1. Parse all models and YAML. 2. Resolve every ref('x') / source('a','b'). 3. Order execution so upstream models run before downstream ones. 4. Power selectors like dbt run --select +fct_orders+ (parents, itself, children).

Why it matters

• Correct run order without manual scripts
• Impact analysis: "if I change stg_orders, what breaks?"
• Selective runs and CI on changed subgraphs
• Lineage in dbt docs

Interview tip: Contrast with Airflow DAGs: Airflow orchestrates tasks/jobs; the dbt DAG describes data dependencies among SQL models inside the warehouse transform layer.