knowledge-base

To turn research into a dbt technical specification, you distill findings into a structured document defining sources, driving tables, joins, filters, and grain. This formalizes exploratory data modeling decisions into a reusable implementation plan.

You document dbt model planning fields by creating a technical specification that enforces mandatory fields like Source, Driving table, Joins, Key expressions, Filters, Grain, and Rows. This ensures every data modeling decision is formally captured before implementation.

The best way to prevent SQL drift is to follow an update procedure that ensures SQL follows the technical spec. This clear update workflow guarantees that SQL implementations always align with the documented data modeling decisions.

Yes, you can use a technical spec for analytics workstreams outside of dbt. The specification framework applies to any analytics project requiring a documented plan with sources, joins, filters, and evidence to guide implementation.

Yes, a data modeling technical spec requires a defined Build Order. It enforces this alongside mandatory per-model planning fields and a Decisions section to sequence SQL implementation and ensure structured project execution.