DBT Architect-AI-guided dbt project optimization

Example

A data engineer wants to optimize model performance by structuring their dbt project with an efficient model hierarchy and clear separation between staging, intermediate, and final layers.

Scenario

DBT Architect provides detailed guidance on best practices like adhering to the dbt layer convention (e.g., `stg_`, `int_`, `fact_`, etc.), optimizing model materializations (like incremental models), and using configurations like `on_schema_change` for automated updates.

Example

A new startup is setting up their data pipeline and wants to structure a dbt project that can scale as their data volumes grow.

Scenario

DBT Architect would advise on organizing folders, defining clear naming conventions, and establishing modular SQL models to support both current needs and future scalability. This might include recommendations on splitting models by domain (e.g., finance, marketing) and implementing effective CI/CD workflows.

Example

An analytics team seeks to leverage dbt's advanced features like macros, hooks, and Jinja templating to automate repetitive tasks and enforce standards across models.

Scenario

DBT Architect would guide the team through writing custom macros to standardize calculations, using pre- and post-hooks for quality checks, and implementing Jinja templates for parameterized model generation. The advice would include specific examples and troubleshooting tips tailored to their environment.