dbt

How to Use dbt

A beginner-friendly guide to getting started with dbt in 2026.

Price Free plan available
Best For data teams, analytics engineers, bi teams
Rating ★★★★★ 4.6/5

Getting Started: Step by Step

1

Create your dbt account

Visit dbt's website and sign up for a free account. You'll need an email address to get started.

2

Set up your workspace

Once signed in, configure your dbt workspace. Set your preferences, invite team members if needed, and customize the interface to match your workflow.

3

Explore sql-transformations

One of dbt's key features is sql-transformations. Navigate to this feature and experiment with it to understand how it fits into your workflow.

4

Explore data-testing

One of dbt's key features is data-testing. Navigate to this feature and experiment with it to understand how it fits into your workflow.

5

Explore documentation

One of dbt's key features is documentation. Navigate to this feature and experiment with it to understand how it fits into your workflow.

6

Integrate with your existing tools

Connect dbt with the other tools you use daily. Most integrations can be set up in the settings or integrations panel.

7

Start using it for real work

Now that you're set up, start using dbt for actual tasks. The best way to learn is by doing — don't worry about getting everything perfect right away.

Pro Tips

  • Start with the free plan or trial to explore dbt's capabilities before committing to a paid subscription.
  • Use keyboard shortcuts to speed up your workflow — most tools have extensive shortcut systems.
  • Check dbt's official documentation and community forums for advanced tips and best practices.
  • Review your workflow after 2 weeks of use and adjust your setup based on what's working and what isn't.

Key Features to Explore

sql-transformations
data-testing
documentation
version-control
scheduling
lineage
metrics-layer

Alternatives to Consider

If dbt isn't the right fit, here are some similar tools:

Ready to Try dbt?

Data transformation tool enabling analytics engineers to transform raw data in warehouses using SQL with version control, testing, and documentation.

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