Accelerate Data Pipeline Dev with Dagster Components

Introducing Dagster Components, a simplified approach to developing and managing your data pipelines

We're excited to announce the preview of Dagster Components, a new approach to developing and managing your data pipelines. Dagster Components empowers data teams to rapidly create, configure, and scale data workflows without being bogged down by complex code or tedious setup tasks.

Why We Built Components

At Dagster, we've always believed in building a better developer experience for data engineers. Components streamlines data pipeline development, making it faster and easier than ever to set up and scale workflows. Our simplified, standardized project structures and YAML-based definitions allow both new and experienced users to rapidly build sophisticated pipelines.

What Are Dagster Components?

Dagster Components offer a simplified, structured approach to defining and managing your Dagster projects, helping teams move from "Hello World" to sophisticated, scalable pipelines effortlessly. Paired with our new CLI experience, dg, Dagster Components brings our best-in-class developer experience to the next level.

With Components, you get:

An opinionated project structure optimized for clarity and scalability.
Reusable, configurable building blocks that minimize boilerplate and speed up pipeline creation.
A streamlined, class-based Python interface and YAML-based DSL that reduce the need for deep Python expertise.

By leveraging these powerful abstractions, you can focus more on your data, and less on the underlying orchestration mechanics.

Why Use Dagster Components?

Accelerated Onboarding & Productivity

Reduce setup times and complexities. With Dagster Components, teams can create libraries of components for internal usage, while ensuring appropriate guardrails are set. Teams ramp up faster and see immediate value, whether adopting Dagster as a new data platform or bringing stakeholders onto an existing Dagster implementation.

Unified CLI Experience

dg combines project initialization, scaffolding, and management into a single, cohesive tool, providing consistent experiences from OSS to Dagster Plus.

Low-Code Convenience, High-Code Power

YAML definitions simplify most scenarios, while Python-based customization ensures flexibility when your pipelines require advanced logic.

AI-Ready

LLMs operate at their best when given constraints and structure. Dagster Components is built from the ground up to work alongside your favorite AI tools, from Copilot to Cursor, Claude Code to Cline. An MCP server will ship alongside Components to enable your favorite tools to seamlessly integrate with Dagster.

Getting Started with Dagster Components

Dagster Components introduces the new unified CLI tool, dg, to streamline project creation and management. The best place to get started with dg and components is with our documentation.

Once you have installed dg and created a project, you can quickly scaffold assets using components with dg. This component can then be customized using YAML, which makes adopting Dagster easier for a wider variety of users and use cases.

For example, if you are a keen birdwatcher, you may have created an asset for a survey of birds for each year, which can lead to duplicated code:

@dg.asset(kinds=["python"], group_name="raw_data")
def checklist_2020(context: dg.AssetExecutionContext):
    extracted_names, elapsed_times = download_and_extract_data(
        context, constants.CHECKLIST_2020
    )
    return dg.MaterializeResult(
        metadata={
            "names": extracted_names,
            "num_files": len(extracted_names),
            "elapsed_time": elapsed_times,
        },
    )


@dg.asset(kinds=["python"], group_name="raw_data")
def checklist_2023(context: dg.AssetExecutionContext):
    extracted_names, elapsed_times = download_and_extract_data(
        context, constants.CHECKLIST_2023
    )
    return dg.MaterializeResult(
        metadata={
            "names": extracted_names,
            "num_files": len(extracted_names),
            "elapsed_time": elapsed_times,
        },
    )

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Now, you can abstract the logic that builds this asset, creating a YAML file per checklist:

type: birds_dot_csv.lib.BirdChecklist

attributes:
  name: checklist_2023
  url: "https://path.to/data/June2023_Public.zip"

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This abstraction can be extended to other integrations as well. Setting up a dbt project can now be as simple as a few lines of code. In this example, the dbt project's path is set, along with a definition of how to translate the dbt model to an asset key.

type: dagster_dbt.DbtProjectComponent

attributes:
  project: "{{project_root}}//dbt/birddbt"
  translation:
    key: "{{node.name}}"

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With components, you can create sophisticated pipelines just by modifying YAML configurations instead of writing extensive Python code, all while still having the full power of Python at your disposal.

Guardrails on Rails

We believe that a low floor doesn't mean a low ceiling. While we provide the convenience of a YAML framework, Dagster Components also supports powerful customizations through Python when deeper control is required. This gives you the flexibility of low-code convenience with the power of Python-based customization when necessary.

You can create rich, powerful abstractions for your team, or rely on a marketplace of components built by Dagster and our partners for the same quality of integrations you've come to expect, but with a simplified implementation. A built-in documentation feature allows you to easily document, browse, and understand components and their attributes to make developing easier.

Another side-effect of providing guardrails that help you write easier to maintain pipelines is that these same benefits enable AI-code-gen experiences. Being able to provide an LLM with context and constraints can finally unlock AI-assisted pipeline building that doesn't feel like a drag. While most LLMs struggle to build sufficiently complex pipelines in a totally free framework, when constrained through the component system we've found that LLMs perform remarkably well.

With an upcoming Model Context Protocol (MCP) Server, Dagster's integration with the latest AI code-editors will only improve.

What's Next?

Dagster Components are currently in preview, and your feedback will shape its future! Give it a try, and tell us about your experiences. Check out the detailed guides below to get started:

Have feedback or questions? Start a discussion in Slack or Github.

Interested in working with us? View our open roles.

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