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In this step you will setup the environment needed for running the tutorial and using Elementary.

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Before you start:

PrerequisitesFor the tutorial you will need:
  1. A working Python installation
  2. pip installer for Python
  3. Access and credentials to a data warehouse supported by Elementary
We also recommend you work with a Python virtual environment.

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Setting Up Your Tutorial Project:

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1. Initialize a New dbt Project:

Name the project elementary_tutorial:
dbt init elementary_tutorial
Ensure that you have a working connection to your DWH. To troubleshoot, you can run dbt debug.

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2. Download tutorial sample data and models

We created a dbt project and sample data for the tutorial. Download the files here.

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3. Copy files to the elementary_tutorial project

Copy the following downloaded directories to your local elementary_tutorial project:
  • Replace the /seeds folder in the project with the downloaded /seeds directory.
  • Replace the /models folder in the project with the downloaded /models directory.

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4. Seed Data to the DWH

Populate the sample data:
dbt seed

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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.

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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.
Make sure you name the project elementary_tutorial.

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3. Connect to the Tutorial Repo

Connect to the forked repository.

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4. Seed Data to our DWH

After creating a branch, we will populate our DWH with the sample data:
dbt seed

Now, let’s add Elementary’s package into our project.