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.

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Powering data at companies like

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.

"You won't need to run a bunch of complicated infrastructure like docker containers to run this locally like you would with Airflow."

Rob Teeuwen

Data Science Lead

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

Talk to an Airflow migration expert