Course prerequisites

This course is geared towards users who want to get the most out of their Dagster code. You don't need to be an expert in Dagster, but you should feel comfortable with the basics.

Intermediate level

  • Dagster familiarity

    You'll need to know the basics of Dagster to complete this course. We recommend completing check out the Dagster Essentials course if you've never used Dagster before or want a refresher before getting started.

  • Python Knowledge

    While you don’t need to be a Python expert to get started, you do need some Python familiarity to complete this course and use Dagster. In Lesson 2, we’ll cover Dagster’s specific installation requirements. Here are some Pythonic concepts used, along with resources to learn about them:

Course curriculum

    1. About this course

    2. Project preview

    3. Testing

    4. Testing data applications

    1. Prerequisites and installation

    2. Set up local

    3. Set up Codespace

    1. Overview

    2. Unit tests

    3. Testing dependencies

    4. Knowledge check

    5. Testing assets with run configurations

    6. Knowledge check

    7. Testing asset output types

    8. Testing assets with execution context

    1. Overview

    2. Testing with mocks

    3. Testing with resources

    4. Knowledge check

    5. Materializing resource tests

    6. Mocking resources for testing

    1. Overview

    2. The limits of mocks

    3. Integration tests

    4. Knowledge check

    5. Integration resources

    6. Configuring integration tests

    7. Integration fixtures

    1. Overview

    2. Asset checks

    3. Definitions

    4. Dagster objects

    5. Knowledge check

    6. Code locations

Skills you'll gain in this course

  • The importance of tests in data applications
  • How to use unit, mock and integrations in tests
  • Best practices for tests in Dagster

Reviews from our Fans!

5 star rating

Great resource to get started with testing in Dagster!

Davis Townsend

Coming from a heavy dbt background, I was pretty nervous about diving into Dagster testing after just finishing the Dagster Essentials course. In dbt, testing feels pretty straightforward with built-in tests and Great Expectations, but I wasn't su...

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Coming from a heavy dbt background, I was pretty nervous about diving into Dagster testing after just finishing the Dagster Essentials course. In dbt, testing feels pretty straightforward with built-in tests and Great Expectations, but I wasn't sure how testing would work in this more Python-heavy world. This course was exactly what I needed to bridge that gap. The instructor did a great job explaining how Dagster testing compares to what I'm used to - instead of just writing YAML configs for tests, you're actually writing proper Python test functions, which felt intimidating at first but ended up being way more flexible. The asset testing section was a game-changer for me since I'm used to thinking about dbt models, and learning how to test Dagster assets felt like a natural extension. The mocking section was completely new territory (you don't really mock things in dbt), but they explained it in a way that made sense for data work. What I loved most was how they showed integration testing for entire pipelines - something that's always been tricky with dbt projects. By the end, I felt confident that I could actually implement testing in our Dagster migration project instead of just hoping our data transformations work correctly. If you're making the jump from dbt to Dagster like I am, this course makes testing feel way less scary.

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