Designing User-Friendly Dagster Components

We recently introduced components as a way to dynamically generate Dagster definitions from configuration. Out of the box, Dagster provides components for common integrations with popular tools like dbt and Fivetran. But components are more than just integrations, they’re a generalized framework that lets anyone build custom software for their data platform.

We wanted to encourage our users to author custom components and streamlined the process through the dg CLI. But designing components can feel a bit different from other areas of data platform engineering. When you build a component you are usually building it for others and want to ensure a great development experience for your users.

So if you’re building your first component and want to ensure that it is a success, here are a few best practices and guiding principles we keep in mind when building with components.

Focus on one type of user

When designing components, always keep your end users top of mind. Those users might be colleagues in different departments, or you might be the primary user, but in every case, it’s critical to consider how people will interact with the component.

A key part of this is deciding what to expose and a lot of this depends on your intended audience. Technical users may want access to every detail of the component for greater flexibility, while non-technical users may find the same options confusing or overwhelming.

Always design with a clear persona in mind. You can then shape the interface and functionality to meet the needs of that role. Keeping components focused on one intended audience helps ensure they remain approachable, usable, and effective.

Design your interfaces first

As you think about your users, start by designing your interfaces. These are the configurable parts of your component, the fields and inputs your users will set when creating a new instance of the component. A well-designed interface helps guide users toward the right inputs while hiding unnecessary complexity.

It’s also important to consider the granularity of your component as that will influence how you design your interface. Imagine you’re designing a component to generate a cookbook. The main entity in the cookbook is a recipe, but each recipe also contains a set of ingredients.

When building components, you want to model your more complex types and see how they weave together. The models for a cookbook might look like this:

import dagster as dg

class Ingredient(dg.Model):
    name: str = dg.Field
    quantity: int = dg.Field
    unit: str = dg.Field

class Recipe(dg.Model):
    title: str = dg.Field
    serves: int = dg.Field
    prep_time: int = dg.Field
    cook_time: int = dg.Field
    ingredients: list[Ingredient] = dg.Field

‍

By carefully designing your models and fields, you gain more control over how users interact with the component and can nudge them toward structured, predictable inputs that are easier to validate and process.

Another consideration is initialization frequency. Ask yourself: how often should this component be created? In the cookbook example, you’d expect a new cookbook to be initialized only when creating an entirely new collection, not each time a recipe is added. That’s why cookbook_title is modeled as a scalar value (str), while recipes is modeled as a vector (list).

class Cookbook(dg.Component, dg.Model, dg.Resolvable):
    cookbook_title: str = dg.Field
    recipes: list[Recipe]
    ...

‍

Do not overload components

It’s possible to generate an entire data platform from a single component but that doesn’t mean you should. Just like in other areas of software engineering, components should follow the single-responsibility principle. A component that tries to do everything often ends up with a confusing interface and becomes difficult to maintain.

One signal that your component is getting too large is a muddled interface. If users have to wade through too many fields or options, it may be time to split the functionality into smaller, more focused components.

Another red flag is an overuse of conditionals. If your component’s interface includes numerous boolean flags that control whether Dagster objects are added to the final Definitions, it’s often a sign that the component is trying to do too much. In these cases, breaking the logic into multiple components can make the design cleaner, easier to use, and more maintainable.

Create modular components

Instead of building increasingly large components, focus on designing modular components. Components should be small, composable building blocks that can be layered and combined to unlock richer functionality. This approach keeps your interfaces simple while still giving you a powerful environment to work in.

Modularity also makes it easier to tailor components to specific personas. For example, our cookbook component provides the perfect interface for people responsible for cooking, but it may not meet the needs of others involved in managing a kitchen. By keeping components modular, you can design purpose-built tools for different roles without overloading a single interface.

Writing tests for components

Like any piece of software, writing tests for your components can help with their maintainability. Testing components is slightly different from testing other Dagster objects because components aren’t initialized until they’re scaffolded. Fortunately, Dagster provides a handy utility, create_defs_folder_sandbox, that makes it easy to spin up a temporary directory for testing.

For example, here’s a simple test that ensures a defs.yaml file is created when the Cookbook component is scaffolded:

from dagster.components.testing.utils import create_defs_folder_sandbox
from components_education.components.cookbook import Cookbook

def test_scaffold_cookbook():
    with create_defs_folder_sandbox() as sandbox:
        defs_path = sandbox.scaffold_component(component_cls=Cookbook)

        # Ensure the defs.yaml file exists
        assert (defs_path / "defs.yaml").exists()

This is a good starting point, but we can take testing further by initializing a component with an example configuration and verifying that it produces the expected Dagster definitions:

import dagster as dg
from dagster.components.testing.utils import create_defs_folder_sandbox
from components_education.components.cookbook import Cookbook

cookbook_yaml_config = {
    "type": "components_education.components.cookbook.Cookbook",
    "attributes": {
        "cookbook_title": "Test Cookbook",
        "recipes": [
            {
                "title": "Test Recipe",
                "serves": 4,
                "prep_time": 10,
                "cook_time": 15,
                "ingredients": [
                    {
                        "name": "Test Ingredient",
                        "quantity": 1,
                        "unit": "Test Unit"
                    }
                ],
            },
        ]
    }
}


def test_cookbook_component():
    with create_defs_folder_sandbox() as sandbox:
        defs_path = sandbox.scaffold_component(component_cls=Cookbook)
        sandbox.scaffold_component(
            component_cls=Cookbook,
            defs_path=defs_path,
            defs_yaml_contents=cookbook_yaml_config
        )

        # Check that all assets are created
        with sandbox.build_all_defs() as defs:
            assert defs.resolve_asset_graph().get_all_asset_keys() == {
                dg.AssetKey(["test_recipe"]),
            }

With this approach, you’re not just verifying that the component scaffolds correctly, you’re also confirming that it initializes and produces the assets correctly.

We can extend this test even more by confirming our scaffolded assets execute properly:

            # Ensure that the assets execute correctly
            result = dg.materialize(
                assets=[
                    defs.get_assets_def(dg.AssetKey(["test_recipe"])),
                ],
            )
            assert result.success

This gives you confidence that your component behaves the way users will expect in a real Dagster environment:

Handle metadata with post_processing

Dagster supports rich metadata, which makes it easier to organize your data platform and keep everything discoverable. While you could expose metadata fields directly in your component interfaces, a cleaner and more flexible approach is to use the post_processing section of a component:

type: components_education.components.cookbook.Cookbook

attributes:
  ...

post_processing:
  assets:
    - target: "*"
      attributes:
        group_name: "ingest"
        tags:
          department: "marketing"
          owner: "analytics_engineering"

Here, metadata is attached at the component level, ensuring that all assets generated by the component are consistently tagged and grouped. This allows you to:

• Organize assets logically (e.g., by department or function).
• Improve discoverability by applying tags and groupings that reflect how your team works.
• Avoid duplication of effort, since you don’t need to hard-code metadata into your definitions.

By leaning on post_processing, you get a cleaner, more centralized way of managing metadata without cluttering your component interfaces or scattering metadata logic across definitions.

Summary

We’re only just getting started with components at Dagster, and we’re excited to see the creative ways our users will apply them to their data platforms. Components open up a powerful new layer of flexibility and we believe they’ll unlock patterns we haven’t even imagined yet.

If you’re beginning your journey with components, we hope these tips give you a strong foundation and help you get the most out of what they offer. With thoughtful design and testing, components can become some of the most reusable, maintainable, and impactful parts of your data platform.