How does dbt manage dependency management between models?

dbt manages dependency management between models through the creation of a directed acyclic graph (DAG). A DAG is a type of graph that ensures there are no cycles, meaning you cannot revisit the same node once it has been traversed. In the context of dbt, each model is represented as a node, and the dependencies between models are represented as edges connecting these nodes.

This structure is beneficial because it allows dbt to determine the order in which models should be run. When one model depends on another, dbt can see this relationship through the DAG and build the models in the correct sequence, ensuring that all dependencies are addressed before final output. This feature effectively manages complex relationships within a dataset, providing clarity and efficiency in transformation workflows.

The other options do not align with dbt's approach to dependency management. A relational database is not specifically used to manage these dependencies within dbt; it stores the data instead. Automated scripts could assist in model creation or updates but do not inherently manage relationships between models. Maintaining a spreadsheet might be useful for tracking information but would lack the dynamic and automated functionality that a DAG provides in managing dependencies between dbt models.