AI-native DataOps platform
Data your team trusts.
AI that runs on it.
Dagster is the operational layer that structures how data is built, observed, and delivered, so both teams and AI agents can rely on it.
Adopted worldwide
Bad data breaks decisions, not just pipelines.
Reliable data infrastructure is the foundation for faster teams, sharper decisions, and AI that actually delivers.
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From operational context to confident action
Dagster+AI runs on the context Dagster already has: assets, runs, lineage, freshness, failures, and automation history, so teams can diagnose issues, explain behavior, and take action faster and more confidently than ever.
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The Platform
The trust layer for your entire stack
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Build and run asset-based pipelines across any tool in your stack. Engineers, stakeholders, and agents all work from the same operational foundation.
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See lineage, dependencies, and data health across your entire platform, not just inside a single tool. Signals become context, and context drives action.
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Turn operational context into action with Dagster+ AI. With Compass, teams can build and ship governed data agents on top of trusted data workflows.
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Grow with reusable components, shared standards, and built-in guardrails. As more teams, tools, and workloads come online, your platform becomes more coherent, not more fragile.
Run your pipelines with full visibility into what’s inside them
When something breaks in your data, the problem usually isn't the pipeline, it's that nobody could see it coming. Dagster attaches lineage, quality signals, and dependency context to every asset so your team always knows the state of their data.
After Dagster
- You catch problems early and understand their impact instantly
- Lineage, dependencies, and health are built into every asset
- AI has the context it needs to make your data team faster
Before Dagster
- You spend more time reacting to issues than shipping
- Lineage is an afterthought, reconstructed after something goes wrong
- AI is layered onto a platform that lacks context and reliability
Scale your platform as you build
Branch deployments
Ship pipeline changes without putting production at risk. Every change validated in a production-like environment before it touches real data.
Composability + guardrails
Platform teams define standards once. Downstream teams and AI-assisted workflows build on top. Consistent, testable, governed by design.
Hybrid deployment
Run compute in your own infrastructure while Dagster manages the control plane. Cloud, on-prem, hybrid — without re-architecting for compliance.
Built-in observability
Real-time monitoring, asset health dashboards, and failure context out of the box. Know what broke, why, and what it affects before stakeholders do.
dbt + Snowflake native
First-class integrations, not bolt-on connectors. Existing dbt models and Snowflake assets orchestrated, monitored, and cataloged without custom glue code.
AI-ready developer experience
Local development, fast iteration, a code-native approach ready for LLM and agent workflows. A platform that won't need to be replaced when AI becomes central to the stack.
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Reliable data isn’t just a nice-to-have.
Start with the platform that makes your entire data stack coherent, observable, and trustworthy: for your team and for AI.
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Frequently asked questions
What is Dagster?
Dagster is an AI-native DataOps platform that orchestrates, observes, and activates data across your entire stack. Unlike traditional schedulers that only track whether a job finished, Dagster understands the assets those jobs produce by attaching lineage, quality signals, and dependency context to every piece of data your team builds and relies on.
How is Dagster different from Apache Airflow?
Dagster is asset-centric, Airflow is task-centric. In Airflow, pipelines are defined as sequences of tasks and Dagster defines pipelines by the data assets they produce. This means Dagster can automatically track lineage, surface data health, and tell you the full blast radius of a failure before it reaches anyone downstream. Airflow can tell you a job failed; Dagster can tell you what broke, why, and what depends on it.
Does Dagster support dbt, Snowflake, and Fivetran?
Yes. Dagster has first-class, native integrations with dbt, Snowflake, and Fivetran. Existing dbt models and Snowflake assets can be orchestrated, monitored, and cataloged within Dagster without custom glue code. The result is a single operational view across your entire stack, from ingestion to transformation to delivery.
Can Dagster run in my own infrastructure?
Yes. Dagster supports hybrid deployment, which means you can run compute in your own infrastructure (cloud, on-premises, or hybrid) while Dagster manages the control plane. This lets organizations meet compliance and data residency requirements without re-architecting their entire stack.
Is Dagster open source?
Yes. Dagster's core orchestration framework is open source and available on GitHub. Dagster+ is the managed cloud offering that adds enterprise features like branch deployments, hybrid deployment, role-based access control, cost insights, and built-in observability with a free tier to get started.
Where should I start if I am new to Dagster?
Dagster University is a great place to learn about Dagster essentials. You can sign up for a free 30 day trial, and get started on your Dagster journey with this quickstart guide.
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