It's Time to Move Beyond Airflow.
Dagster accelerates your data teams, unifies all of your Airflow instances, and simplifies your stack into a single control plane.
What makes Dagster click with enterprise teams
Dagster fits into the way modern teams work, with the flexibility, visibility, and guardrails enterprises need to move fast without breaking things. Here’s what makes it a no-brainer for the teams we work with:
Accelerate
Engineers building data pipelines in Dagster are 2x more productive than those using Airflow and benefit from a modern SDLC and delightful developer experience.
Unify
Dagster supercharges cross-team collaboration with federated orchestration, observability and lineage across Dagster pipelines and all Airflow instances.
Simplify
Dagster reduces the number of tools in the data stack through its built-in data cataloging, observability, data quality, and cost management features.
Accelerate from pipelines to platforms
Modern data engineering requires a fresh approach
Fast local development and unit testing
Dagster brings modern software engineering practices to data orchestration with lightning-fast local development, and comprehensive unit-testing. Build, test, and debug your data pipelines on your laptop because data engineering is software engineering.
Low code data pipelines
You can build your pipelines using Dagster’s asset-oriented Python framework or a declarative YAML-based workflow. Build pipelines in minutes, not days, so you can spend time on what matters.
A sandbox for every pull request
Airflow wants you to test in production, but Dagster’s branch deployments mean you can spin up isolated environments that mirror production. Test your changes end-to-end in a complete sandbox before merging to main.
Cloud native, multitenant architecture
Built for modern cloud environments, Dagster scales effortlessly to support your entire organization. Our multitenant design allows different teams to deploy and maintain their data assets independently within a unified platform.
Any language, any technology
Why should your orchestrator dictate your technology choices? Dagster integrates seamlessly with your existing tools and languages. Whether using Python, SQL, Spark, or anything else, Dagster brings everything together in one unified view.
Unify your Airflow clusters with Dagster
Skip the painful Airflow 3 rewrite and modernize your data platform in 3 easy steps
Integrate
With just a few lines of code, you can observe and govern your Airflow DAGs from all your Airflow instances in a single location. Break down the data silos without changing a single line of Airflow code.
Build
Build new data pipelines with Dagster's modern developer experience, or add data quality checks to existing Airflow DAGs. All without touching the existing Airflow code. Migrate with Airlift
Refine
With Dagster's rich observability and operational tooling, you'll no longer need several components of your stack. And as data pipelines are incrementally migrated from Airflow to Dagster, you can shut down your legacy Airflow instances.
Simplify and modernize your stack
Dagster goes well beyond Airflow and offers rich capabilities for data management
Data catalog
Dagster's data catalog lets technical stakeholders discover data assets and explore their lineage, operational state, and other metadata.
Data quality
You can incrementally add data quality checks to your existing Airflow DAGs, observe the health of your data pipelines, and make runtime decisions based on data quality.
Cost management
Dagster integrates a rich cost management suite, enabling both data platform owners and their stakeholders to manage their spend on data tools like Snowflake.
Incremental migration
Dagster provides tooling to incrementally migrate DAGs from legacy Airflow instances to modern Dagster code. We also provide professional services to migrate your DAGs for you.
See how to use Airlift to easily operate or migrate Airflow in Dagster.
View Airflow execution alongside your Dagster workflows
Turn existing Airflow DAGs into Dagster assets
Consolidate multiple Airflow instances together in one place
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Turn your data engineers into rockstars
