Setting Up your Tutorial Environment
In this step you will setup the environment needed for running the tutorial and using Elementary.
Before you start:
Prerequisites
For the tutorial you will need:
- A working Python installation
- pip installer for Python
- Access and credentials to a data warehouse supported by Elementary
We also recommend you work with a Python virtual environment.
Setting Up Your Tutorial Project:
dbt core
dbt core
1. Initialize a New dbt Project:
Name the project elementary_tutorial:
Ensure that you have a working connection to your DWH. To troubleshoot, you can run dbt debug.
2. Download tutorial sample data and models
We created a dbt project and sample data for the tutorial. Download the files here.
3. Copy files to the elementary_tutorial project
Copy the following downloaded directories to your local elementary_tutorial project:
- Replace the
/seedsfolder in the project with the downloaded/seedsdirectory. - Replace the
/modelsfolder in the project with the downloaded/modelsdirectory.
4. Seed Data to the DWH
Populate the sample data:
dbt cloud
dbt cloud
1. Fork the Elementary Tutorial Repo
We created a dbt project to help us with the tutorial. The project is an extended version of the dbt jaffle shop project. It contains seeds and models that we will use to run Elementary’s tests on, and collect artifacts.
In order to load the tutorial project into your dbt cloud environment, you need to fork the repo and connect a new project to it.
To fork the the tutorial repo, go to the tutorial repo and click on the fork button.
2. Create a new dbt Project
To create a new project in dbt cloud, go to Account Settings and click on the New Project button under the Projects section.
Create a new project named elementary_tutorial.
elementary_tutorial.3. Connect to the Tutorial Repo
Connect to the forked repository.
4. Seed Data to our DWH
After creating a branch, we will populate our DWH with the sample data:
Now, let’s add Elementary’s package into our project.