🧱 Module 3: Create Your First dbt Project and Connect to a Data Warehouse

📘 Introduction

Now that dbt CLI is installed, it’s time to create your first dbt project. In this module, you’ll:

• Initialize a dbt project

• Connect it to a data warehouse (we’ll use Snowflake or PostgreSQL)

• Understand the project folder structure

• Create and run your first model

Let’s dive in and turn raw data into analytics-ready models using just SQL.

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🧰 What You’ll Need

• dbt CLI installed (dbt --version)

• Access to Snowflake or Postgres (other adapters also work)

• Terminal or command prompt

• Basic SQL knowledge

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🚀 Step-by-Step: Create a dbt Project

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🔹 Step 1: Initialize Your Project

In terminal or CMD, navigate to your workspace and run: 

dbt init my_dbt_project

Replace my_dbt_project with your preferred name.

🔹 Step 2: Choose Your Adapter

During the init process, dbt will ask you to choose a warehouse adapter.

For example:

• snowflake for Snowflake

• postgres for PostgreSQL

Follow the prompts and confirm project setup.

🔹 Step 3: Understand Project Structure

After setup, you’ll see folders like: 

my_dbt_project/

├── dbt_project.yml      # Project config file

├── models/              # Where your SQL models live

├── snapshots/           # Optional - point-in-time copies

├── seeds/               # Static CSV data files

├── macros/              # Reusable SQL logic (Jinja)

└── target/              # Output folder (autogenerated)

✅ models/ is where you’ll spend most of your time.

🔹 Step 4: Set Up Your Profile

dbt connects to the warehouse using a profiles.yml file.

Location:

• Windows: C:\Users\<yourname>\.dbt\profiles.yml

• Mac/Linux: ~/.dbt/profiles.yml

Snowflake Example:

my_dbt_project:

  target: dev

  outputs:

    dev:

      type: snowflake

      account: your_account

      user: your_user

      password: your_password

      role: your_role

      database: your_database

      warehouse: your_warehouse

      schema: analytics

      threads: 1

Postgres Example:

my_dbt_project:

  target: dev

  outputs:

    dev:

      type: postgres

      host: localhost

      user: your_user

      password: your_password

      port: 5432

      dbname: your_db

      schema: analytics

      threads: 1

🔹 Step 5: Test the Connection

Inside your project folder, run:

dbt debug

✅ You should see:
All checks passed! Connection is working.

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🔹 Step 6: Create and Run Your First Model

Create a file:
📄 models/first_model.sql

Paste this simple model:

SELECT 1 AS id, 'dbt works!' AS message

Run your model:

dbt run

🎉 That’s it! Your first model is live in your data warehouse.

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💡 Pro Tips

• Keep models small and modular — one concept per file

• Use Jinja templating for dynamic logic (we’ll cover this in Module 4)

• Use dbt run --select model_name to run individual models

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📌 What’s Next?

📍 Next Module: Create Modular dbt Models Using SQL + Jinja
You’ll learn to layer models and add reusable SQL logic with Jinja templating.