Module 1 — Setting Up dbt

Provision BigQuery, get dbt running two different ways (cloud and local), and put your project under version control. Five lessons of infrastructure.

This module is about getting your environment standing up. By the end you'll have a BigQuery project, a working dbt installation (either Cloud or local), and the whole thing in Git. The dbt concepts themselves come in Module 2.

What you'll learn

  • How to provision BigQuery for the rest of the course
  • How to set up dbt two different ways: dbt Cloud (browser) and locally with VS Code + the dbt Power User extension
  • How to put your project under version control with Git and GitHub

Lessons

  1. Introduction to dbt — what dbt is, why it matters, and the two setup paths
  2. Prerequisite: Setting up BigQuery — Nexus UI or MCP plan → approval → connected warehouse
  3. Setting up dbt Cloud — separate dbt connection + BigQuery + the Cloud IDE
  4. Setting up dbt locally with VS Code — Python, dbt-bigquery, profiles.yml, dbt Power User
  5. Git and GitHub — version control + the branch → PR → merge workflow

When you're done

You'll have a working dbt project on BigQuery, in version control, with a dbt debug that returns "All checks passed!" — ready to dig into the dbt concepts themselves in Module 2 — dbt Basics.

Start with 1.1 Introduction to dbt.