Dagster vs. Airflow

When Airflow was originally designed in 2014, it was a huge step forward. But data engineering has progressed dramatically since then. Dagster was built from the ground up to equip data teams with the right tools for building and managing a data platform in today’s data ecosystem.

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Why data teams are switching from Airflow to Dagster

Asset-centric development

Dagster’s Software Defined Assets provide an intuitive framework for collaboration across the enterprise. You can focus on delivering critical data assets, not on the tasks of pipelines.

Airflow is task-centric and does not provide asset-aware features or a coherent Python API. It is typically implemented after pipelines have been designed to trigger the required tasks.

Better testing and debugging

Dagster is designed for use at every stage of the data development lifecycle. It facilitates local development, unit testing, CI, code review, staging, and debugging.

Airflow pipelines are harder to test and review outside of production deployments. Many teams working on Airflow end up doing their final testing in production.

Cloud-native infrastructure

Dagster is cloud- and container-native, and designed for today's data infrastructure (ECS, K8s, Docker). Dependencies are easy to manage and upgrades are smooth. Dagster+ provides a turnkey hosting solution.

Isolating dependencies and provisioning infrastructure with Airflow is complex and time consuming.

Here are the main differences between Apache Airflow and Dagster:


Core Focus	Workflow Orchestration	Data Orchestration
Primary Building Block	Tasks	Assets
Safe cross-team collaboration
Partitioned data support	Limited
Sensors isolated from runtime
Cost observability
Basic Alerting
Conditional alerting
Native data quality support
Environment management
Commercial Alternative hosting options

Here are the key differences between Astronomer and Dagster+:


Core focus	Workflow Orchestration	Data Orchestration
Primary building block	Tasks	Assets
Local development support
CI/CD support and dev branching
Environment management	Astonomer's reliance on Docker for deployment creates significant friction.
dbt support		Dagster's support of dbt models is far superior to Astronomer's basic integration.
Alerting
Partitioned data support		In Dagster, asset partitioning is handled as first-class objects.
Backfills	Astronomer's backfills are managed via the CLI	Dagster offers precise control of backfills directly in the UI
Embedded ELT support	Astronomer offers multiple integrations with ELT tools	Dagster offers embedded ELT, with ingestion translated directly to data assets.
Sensors isolated from runtime	Some Astronomer sensors do not return values, making them mostly useless.
Cross-team collaboration		Dagster code locations maintain safe separation of work without limiting cross-team collaboration.
Data catalog		Dagster's built-in asset catalog provides rich metadata and global lineage.
Column-level lineage
Data quality (asset checks)		Dagster Asset Checks support arbitrary quality checks in Python.
Automatic updates on freshness checks		Dagster auto-materialization eliminates redundant compute (experimental feature).
Estimating credit usage	Clearly shows your accrued charges during trial, making it easy to forecast billing.
Operational observability (including costs)		Dagster Insights provide detailed report on operational and cost metrics.
Community size	Very large	Large

“Airflow was built to string tasks together, not provide an overview of all the ways data is flowing or what’s causing issues.”

David Jayatillake

Going with the Airflow

Dagster vs. Airflow

Dagster was designed for the evolving needs of data engineers. Unlike its predecessors, Dagster was built from the ground up with data assets and the full development lifecycle in mind, for a more complete and integrated approach to data pipelines.

Most Dagster users were Airflow users and nearly every Dagster user was an Airflow user at the time they chose Dagster.

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Astronomer vs. Dagster+

Astronomer aims to augment Airflow to enhance developer productivity and optimize operational efficiency at scale. It has helped hundreds of organizations overcome the challenges of deploying and maintaining Airflow open-source.

Dagster Labs developed both Dagster open-source and Dagster+ as a new asset-oriented approach to orchestration. By managing both the definition and the production of data assets, Dagster+ can leverage rich metadata to provide novel features that go well beyond the execution of pipelines.

Read Astronomer vs. Dagster+

Get started with a free 30-day trial

Every Dagster+ trial includes:

Unlimited code locations

Use Dagster code locations to support different projects and multiple teams all within one data platform

Unlimited branch deployments

A temporary deployment built for testing purposes that spins down when a branch is merged

Serverless or Hybrid

Choose between a fully managed Serveless offering or a Hybrid deployment.

Authentication & SSO

Event logging

Unlimited alerts

Automatically trigger Slack or email notifications on events like job failures, sensor failures, or and agent downtime.

Data quality checksLearn more

Insights (Operational observability)Learn more

Embedded ELTLearn more

Declarative asset schedulingLearn more

dbt-native orchestrationLearn more

Easily migrate & run existing Airflow DAGsLearn more

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Ramp up quickly

We offer many resources to get started, including the free trial with starter templates, an active and growing community on Slack, and detailed docs with AI assistant and tutorials.

A great place to start is with our free Dagster University course content.

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Dagster+ for Enterprise

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.

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