Dagster vs Airflow - why data teams are switching (2024)

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.

Get started with Dagster

Try Dagster+ for free

30-day trial. No credit card required.

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.

Dagster vs Airflow - why data teams are switching (2)

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.

Dagster vs Airflow - why data teams are switching (3)

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.

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

Dagster vs Airflow - why data teams are switching (4)

Migrating off Airflow is now a breeze

Dagster provides tooling that makes porting Airflow DAGs to Dagster much easier. Data teams looking for a radically better developer experience can now easily transition away from legacy imperative approaches and adopt a modern declarative framework that provides excellent developer ergonomics.

Find out how

Get started with a free 30-day trial

Every Dagster+ trial includes:

Dagster vs Airflow - why data teams are switching (5)

Unlimited code locations

Dagster vs Airflow - why data teams are switching (6)

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

Dagster vs Airflow - why data teams are switching (7)

Unlimited branch deployments

Dagster vs Airflow - why data teams are switching (8)

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

Dagster vs Airflow - why data teams are switching (9)

Serverless or Hybrid

Dagster vs Airflow - why data teams are switching (10)

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

Dagster vs Airflow - why data teams are switching (11)

Authentication & SSO

Dagster vs Airflow - why data teams are switching (12)

Event logging

Dagster vs Airflow - why data teams are switching (13)

Unlimited alerts

Dagster vs Airflow - why data teams are switching (14)

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

Dagster vs Airflow - why data teams are switching (15)

Data quality checksLearn more

Dagster vs Airflow - why data teams are switching (16)

Insights (Operational observability)Learn more

Dagster vs Airflow - why data teams are switching (17)

Embedded ELTLearn more

Dagster vs Airflow - why data teams are switching (18)

Declarative asset schedulingLearn more

Dagster vs Airflow - why data teams are switching (19)

dbt-native orchestrationLearn more

Dagster vs Airflow - why data teams are switching (20)

Easily migrate & run existing Airflow DAGsLearn more

Dagster vs Airflow - why data teams are switching (21)Dagster vs Airflow - why data teams are switching (22)Dagster vs Airflow - why data teams are switching (23)Dagster vs Airflow - why data teams are switching (24)Dagster vs Airflow - why data teams are switching (25)Dagster vs Airflow - why data teams are switching (26)Dagster vs Airflow - why data teams are switching (27)Dagster vs Airflow - why data teams are switching (28)Dagster vs Airflow - why data teams are switching (29)Dagster vs Airflow - why data teams are switching (30)Dagster vs Airflow - why data teams are switching (31)Dagster vs Airflow - why data teams are switching (32)Dagster vs Airflow - why data teams are switching (33)Dagster vs Airflow - why data teams are switching (34)Dagster vs Airflow - why data teams are switching (35)Dagster vs Airflow - why data teams are switching (36)

Dagster vs Airflow - why data teams are switching (37)Dagster vs Airflow - why data teams are switching (38)Dagster vs Airflow - why data teams are switching (39)Dagster vs Airflow - why data teams are switching (40)Dagster vs Airflow - why data teams are switching (41)Dagster vs Airflow - why data teams are switching (42)Dagster vs Airflow - why data teams are switching (43)Dagster vs Airflow - why data teams are switching (44)Dagster vs Airflow - why data teams are switching (45)Dagster vs Airflow - why data teams are switching (46)Dagster vs Airflow - why data teams are switching (47)Dagster vs Airflow - why data teams are switching (48)Dagster vs Airflow - why data teams are switching (49)Dagster vs Airflow - why data teams are switching (50)Dagster vs Airflow - why data teams are switching (51)Dagster vs Airflow - why data teams are switching (52)

Ready to get started?

Dagster vs Airflow - why data teams are switching (53)

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.

Check out Dagster University.

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.

Dagster vs Airflow - why data teams are switching (2024)
Top Articles
Latest Posts
Article information

Author: Fredrick Kertzmann

Last Updated:

Views: 5987

Rating: 4.6 / 5 (46 voted)

Reviews: 93% of readers found this page helpful

Author information

Name: Fredrick Kertzmann

Birthday: 2000-04-29

Address: Apt. 203 613 Huels Gateway, Ralphtown, LA 40204

Phone: +2135150832870

Job: Regional Design Producer

Hobby: Nordic skating, Lacemaking, Mountain biking, Rowing, Gardening, Water sports, role-playing games

Introduction: My name is Fredrick Kertzmann, I am a gleaming, encouraging, inexpensive, thankful, tender, quaint, precious person who loves writing and wants to share my knowledge and understanding with you.