Introduction to dbt

You build an example warehouse from scratch — creating dbt projects, writing models and adding tests — which is exactly the analytics-engineering workflow that's reshaped data teams. dbt's real contribution is bringing software-engineering discipline to SQL: version control, testing, and reusable models in place of the tangle of hand-written scripts analysts used to maintain. Knowing it is increasingly a baseline expectation for analytics-engineering roles, not a nice-to-have.

It assumes you write SQL and work with data warehouses, so it's not for people who don't. It runs on DataCamp's subscription (roughly $14/month on the annual plan, first chapter free), and hands you a completion certificate — evidence of the learning, not a credential. dbt has settled into a genuine industry standard (as of 2026).

Introduction to dbt covers the concepts and terminology behind dbt, the SQL-based transformation tool that's become standard in modern analytics stacks, by building an example warehouse from scratch — creating dbt projects, writing SQL models, defining dependencies with the ref function, using Jinja templates, and generating documentation.