Dagster or AWS Step Functions? What to know before you choose
AWS Step Functions is a general-purpose workflow orchestration embedded within the AWS ecosystem, but what should you know before deciding on an orchestrator?
Two tools with different foundations
Engineered for data engineers
Dagster is a purpose-built data orchestrator designed for modern, composable data platforms. It is built from the ground up for data engineering and offers native Python development, data asset tracking, flexible scheduling, and integrated testing—making it easier for teams to build, test, and deploy data pipelines.
Infrastructure-oriented, not data-centric
AWS Step Functions is a general-purpose serverless workflow runner that helps developers coordinate AWS services using JSON-based state machines. It’s designed for infrastructure orchestration and application logic.
Where Dagster and AWS Step Functions differ
Built for developer and data teams
Dagster supports a modern development workflow: version-controlled assets, local development, branch deployments, and CI/CD integrations. Step Functions requires working within AWS’s JSON-based definition language or visual editor, which limits developer productivity.
Iterate faster, debug more easily, and collaborate better across teams with Dagster.
Data awareness and lineage
Dagster models data assets directly, enabling lineage tracking, automated re-materializations, and observability that aligns with the way modern data teams operate. Step Functions is task-based and treats data as an external concern.
Automation and scheduling
With Dagster, you get built-in observability, alerts, and quality checks. Dagster+ offers even deeper insights through Dagster Insights. In Step Functions, observability is bolt-on—you’ll need to wire up logging and alerts yourself using CloudWatch and Lambda.
Dagster vs Step Functions Feature breakdown
| | |
|---|---|---|
Primary use case | Help data engineers manage and orchestrate data | Help coordinate AWS services. |
Developer experience | Code-first, Python-native, local testing, branch deployments | SON/YAML DSL, visual designer, limited local dev |
Observability | Asset-centric lineage, metrics, built-in UI | CloudWatch logs, custom setup required |
Data awareness | First-class data assets, lineage, partitions | No concept of data assets or lineage |
Tooling integration | Native support for dbt, AWS, Snowflake and more | Good AWS integrations (Lambda, S3, SNS) Limited data tool integrations |
Deployment flexibility | Hybrid (self-hosted compute + managed UI), cloud, OSS | Fully managed on AWS only |
Best for | Data teams building and scaling data platforms | AWS-native teams orchestrating general workflows |
When to use Dagster instead of Step Functions
Step Functions works well for orchestrating AWS infrastructure, but falls short when applied to data workflows that require context, lineage, and developer-friendly iteration. If you're using Step Functions to schedule dbt runs, Airbyte jobs, or Python scripts, Dagster likely offers a better alternative - with visibility, testing, and modularity built-in.
If your teams need to build a modern data platform and value local dev, CI/CD, and modular code reuse, Dagster is the better choice.
Orchestrate more. Stress less.
Looking for unlimited deployments, advanced RBAC and SAML-based SSO, all on a SOC2 certified platform? Contact the Dagster Labs sales team today to discuss your requirements.
Frequently asked questions
How is Dagster different from a general-purpose workflow runner?
Dagster is built for data engineering: it treats data assets and dependencies as first-class concepts, with lineage and observability aligned to how data teams ship pipelines—not only task success and failure.
Can I run Dagster outside AWS?
Yes. Dagster supports self-hosted and hybrid deployment as well as managed options, so you are not locked to a single cloud’s workflow service.
How does Dagster handle integrations with tools like dbt or Snowflake?
Dagster provides integrations so common data stack tools can be orchestrated with shared context, testing, and UI visibility.
Does Dagster replace CloudWatch for observability?
Dagster gives asset-centric metrics and a built-in UI for runs and lineage; you may still use cloud logging where needed, but you do not rely on wiring everything through generic logs alone.
How do I connect Dagster to external systems for custom logic?
Dagster Pipes lets you bridge Dagster runs with external processes while keeping observability coherent.
Is Dagster only for open-source self-hosted use?
Dagster is open source, and you can self-host or use Dagster+ for managed, enterprise-grade deployment.
What is Dagster+?
Where should I start if I am new to Dagster?
Dagster University is a great place to learn about Dagster essentials. We have a quickstart guide that walks you through your first pipeline in Python with minimal setup.