Skip to main content

Navigating and other resources

We invest heavily and these docs, as well as other mediums to help you level-up as an analytics engineer. Since there are so many different mediums, it's useful to outline how these pieces fit together.

Understanding the role of each medium Docs section

Understanding oriented

Introduce the core concepts of dbt, with examples. Best for new dbt users that want to understand what a particular feature is and how to use it. Reference section

Information oriented

The technical reference for dbt configurations. These docs assume that you have a basic understanding of key concepts. Best for dbt users that already know what a particular feature is, and want to see the exact usage docs. These docs contains advanced examples.

If you're an advanced dbt user, you'll spend most of your time here. Tutorial section

Learning oriented

Provide an way for a new dbt user to get started with dbt FAQs

Problem oriented

Easily indexed common questions that link back to relevant guides and reference docs. Mainly for questions that we can anticipate.

Stack Overflow


Troubleshooting oriented

Specifically, “I’m stuck and don’t know what to do”. We use Stack Overflow to answer these questions since:

  • Stack Overflow has functionality to "upvote" answers and mark questions as resolved.
  • Questions in Stack Overflow are indexed by search engines


Tactic/use-case oriented

How analytics engineers use dbt to solve their tactical problems, e.g.:

  • Version controlling UDFs
  • Writing a custom schema test for not null
  • Snowflake shares + dbt
  • Permission schemes in a data warehouse

Usually these are write-ups where there is no one perfect answer (unlike the “I’m stuck” questions on Stack Overflow), instead, you might need to dig into the “why” or discuss tradeoffs of your approach in these articles.

dbt Blog

Strategy oriented

Bigger picture approaches, where the content is relevant to most data practitioners, not just for dbt users


Sign up at

Community oriented

Create connections with other analytics engineers. Discuss ideas that require opinions, or push the boundaries of what has been done before.