README.md

Dagster University

This is a forked Dagster project made to accompany Dagster University coursework. The difference with this fork is that it uses uv for Python package management and has a Dockerfile to build a Docker image and run a container for local development. This repository accompanies a write-up and instructions to follow along.

• Docker & uv: A Streamlined Approach to Managing Local Dagster Environments

Branches

Branch name	Description
main	Dagster Essentials finished project
module/dagster-and-dbt	Dagster + dbt finished project
module/dagster-and-dbt-starter	Starter project for Dagster + dbt

Getting Started

Once in the Docker Container, within VS Code:

Duplicate the .env.example file and rename it to .env. Then, fill in the values for the environment variables in the file.

Then, start the Dagster UI web server:

dagster dev

Open http://localhost:3000 with your browser to see the project.

Development

Adding new Python dependencies

You can specify new Python dependencies with uv via uv add <python-package>

Unit testing

Tests are in the dagster_university_tests directory and you can run tests using pytest:

pytest dagster_university_tests

Schedules and sensors

If you want to enable Dagster Schedules or Sensors for your jobs, the Dagster Daemon process must be running. This is done automatically when you run dagster dev.

Once your Dagster Daemon is running, you can start turning on schedules and sensors for your jobs.

Deploy on Dagster Cloud

The easiest way to deploy your Dagster project is to use Dagster Cloud.

Check out the Dagster Cloud Documentation to learn more.