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.