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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”
“Somebody magically built the thing I had been envisioning and wanted, and now it's there and I can use it.”
"Dagster is a lot easier to get used to than Airflow or others. Nice UI. Branch Deployments is also a cool feature."
“Dagster brings software engineering best practices to a data team that supports a sprawling organization with minimal footprint.”
“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.”
“Dagster acts as a central plane for understanding data lineage, monitoring asset states, and orchestrating pipelines to update them.”
“Being able to visualize and test changes using branch deployments has enabled our data team to ship faster”
“Somebody magically built the thing I had been envisioning and wanted, and now it's there and I can use it.”
"Dagster is a lot easier to get used to than Airflow or others. Nice UI. Branch Deployments is also a cool feature."
“Dagster brings software engineering best practices to a data team that supports a sprawling organization with minimal footprint.”
“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.”
“Dagster acts as a central plane for understanding data lineage, monitoring asset states, and orchestrating pipelines to update them.”
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.
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.
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.
"You won't need to run a bunch of complicated infrastructure like docker containers to run this locally like you would with Airflow."
"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."
It's very easy to make and immediately test something in Dagster compared to Airflow, where you might need to set up much more complex infrastructure dependencies first.
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.
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.
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.
"You won't need to run a bunch of complicated infrastructure like docker containers to run this locally like you would with Airflow."
"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."
It's very easy to make and immediately test something in Dagster compared to Airflow, where you might need to set up much more complex infrastructure dependencies first.
