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Curious what Dagster+ can do for your data teams?

“Being able to visualize and test changes using branch deployments has enabled our data team to ship faster”

Aaron Fullerton

“Somebody magically built the thing I had been envisioning and wanted, and now it's there and I can use it.”

David Farnan-Williams
Lead Machine Learning Engineer

“Dagster brings software engineering best practices to a data team that supports a sprawling organization with minimal footprint.”

Emmanuel Fuentes

“Dagster has been instrumental in empowering our development team to deliver insights at 20x the velocity compared to the past. From Idea inception to Insight is down to 2 days vs 6+ months before.”

Gu Xie
Head of Data Engineering

“Dagster acts as a central plane for understanding data lineage, monitoring asset states, and orchestrating pipelines to update them.”

Guillaume Tauzin
ML Engineer | Zaphior Technologies

"Dagster is easy to use, it's ELT friendly, can integrate with the main modern tools out of box and allows you to automate whatever you want wherever it is."

Ismael Rodrigues
Lead Data Engineer | C&A Modas (C&A Pay)

“Being able to visualize and test changes using branch deployments has enabled our data team to ship faster”

Aaron Fullerton

“Somebody magically built the thing I had been envisioning and wanted, and now it's there and I can use it.”

David Farnan-Williams
Lead Machine Learning Engineer

“Dagster brings software engineering best practices to a data team that supports a sprawling organization with minimal footprint.”

Emmanuel Fuentes

“Dagster has been instrumental in empowering our development team to deliver insights at 20x the velocity compared to the past. From Idea inception to Insight is down to 2 days vs 6+ months before.”

Gu Xie
Head of Data Engineering

“Dagster acts as a central plane for understanding data lineage, monitoring asset states, and orchestrating pipelines to update them.”

Guillaume Tauzin
ML Engineer | Zaphior Technologies

"Dagster is easy to use, it's ELT friendly, can integrate with the main modern tools out of box and allows you to automate whatever you want wherever it is."

Ismael Rodrigues
Lead Data Engineer | C&A Modas (C&A Pay)

No vendor lock-in—90% of your Dagster code can be reused without it. In just 10 months of use, I’ve already seen multiple instances where Dagster's data observability features helped us catch and resolve issues that would have otherwise gone unnoticed.

Martin Erpicum
Data Engineer

We built a deployment of Dagster in 6 months for a team of 40 analysts and data scientists, all managed by two software engineers, with no dedicated data engineers, for a Fortune 500 company. There is no leaner, cleaner option out there.

Lee Littlejohn
Lead Machine Learning Engineer

Dagster provided out-of-the-box support for deployment and execution on Kubernetes, had built-in support for declarative automation, and provided a UI that allowed data producers and consumers to quickly understand the state of their data assets.

Zach Bluhm
Engineering Manager

"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

"Choosing the right abstraction is the most important decision you can make. Airflow requires you to write configuration as code; Dagster allows you to write code that implements business logic."

Joe Naso
Founder

"Dagster grows with you; it's easy to learn and remains intuitive. Airflow starts and stays hard, making it incredibly demotivating to get started."

Noah Ford
Senior Data Scientist

No vendor lock-in—90% of your Dagster code can be reused without it. In just 10 months of use, I’ve already seen multiple instances where Dagster's data observability features helped us catch and resolve issues that would have otherwise gone unnoticed.

Martin Erpicum
Data Engineer

We built a deployment of Dagster in 6 months for a team of 40 analysts and data scientists, all managed by two software engineers, with no dedicated data engineers, for a Fortune 500 company. There is no leaner, cleaner option out there.

Lee Littlejohn
Lead Machine Learning Engineer

Dagster provided out-of-the-box support for deployment and execution on Kubernetes, had built-in support for declarative automation, and provided a UI that allowed data producers and consumers to quickly understand the state of their data assets.

Zach Bluhm
Engineering Manager

"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

"Choosing the right abstraction is the most important decision you can make. Airflow requires you to write configuration as code; Dagster allows you to write code that implements business logic."

Joe Naso
Founder

"Dagster grows with you; it's easy to learn and remains intuitive. Airflow starts and stays hard, making it incredibly demotivating to get started."

Noah Ford
Senior Data Scientist