dbt-workflow

Automating the dbt workflow involves orchestrating an 8-step pipeline that scans the project directory, validates configurations, discovers macros, researches technical specs, writes SQL, and verifies models to enforce consistent governance.

The standard dbt development workflow is an 8-step sequence encompassing project scanning, validation, macro discovery, research, technical specification, SQL writing, and verification to ensure data engineering consistency and quality checks.

Yes, automated workflow validation requires a dbt-enabled environment with a valid project directory, plus access to your knowledge-base and macros to guide decisions and enforce project governance during the pipeline build.

Enforcing dbt project governance is best achieved by automating a mandatory 8-step workflow that checks for hazards, coordinates dependencies, and uses knowledge base rules to drive decisions across multiple models from stubs to verification.

Yes, an automated dbt workflow supports end-to-end development by coordinating stubs, dependencies, and incremental considerations across multiple models, ensuring all technical specs and SQL writing meet verification standards.

Macro discovery and technical spec generation are required before SQL writing to identify existing reusable macros and define model requirements, ensuring the generated SQL aligns with project governance and knowledge-base guidelines.