In this lesson, we’ll walk you step-by-step through creating and configuring a dbt project on your local machine. Once completed, you’ll have a working scaffolding to build transformations, tests, and documentation.

Prerequisites

If you plan to follow along and practice, ensure you have the following are installed on your system:

  • Python 3.8+
  • pip (or an alternative like uv)

Create and activate a Python virtual environment

Before installing dbt or any Python package, it’s best to use a virtual environment to keep your project’s dependencies isolated and organized.

python -m venv dbt-env 
# Activate in PowerShell: 
.dbt-envScriptsActivate.ps1
python -m venv dbt-env 
source dbt-env/bin/activate

Upgrade pip and essential tools (Optional)

python -m pip install --upgrade pip setuptools wheel

Install dbt

Once you have created and activated your virtual environment, proceed by installing dbt-core  using the following command:

pip install dbt-core==1.10.13

Note

The command above installs version 1.10.13 replace with the latest version if needed.

Verify Installation

Once the installation is complete, run the following command to verify that DBT is installed correctly

dbt --version

The command output will look like this:

Core:
  - installed: 1.10.13   
  - latest:    1.10.13 - Up to date!

Summary

You’ve successfully set up your local dbt environment and installed dbt-core—now you’re ready to start running dbt projects.

In the next lesson, we’ll explore the dbt project structure . You’ll learn what the key folders and files are, what each one does, and how they work together to keep your transformations clean, organized, and scalable.

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