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.
14x fresher data
Business-critical data freshness improved from 7 hours to 30 minutes
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90x faster onboarding
Developer onboarding shrank from 3 months to 1 day
1k+ models
automated
Over 1000 dbt models have been automated with zero downtime
15x pipeline
efficiency
Execution time has dropped from 2.5 hours to 10 minutes
70% quicker analytics
Game insights are delivered within 15 minutes of the final out
100% automated
Manual operational tasks have been eliminated saving 8 hours a week
Meet Dagster+ AI
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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01
Orchestrate
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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02
Observe
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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03
Activate
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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04
Scale
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.
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How Dagster thinks
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
Enterprise-grade, developer-loved
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.
Get started
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.
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.
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.
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.
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.
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.
New and noteworthy
Webinar
July 9, 2026
How we use AI to get to yes (and no!) 2x faster at Dagster
Learn how Dagster uses AI to build custom demos that deliver a personalized experience for every customer.
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Webinar
April 13, 2026
Multi-Tenancy for Modern Data Platforms
Learn the patterns, trade-offs, and production-tested strategies for building multi-tenant data platforms with Dagster.
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Blog
August 6, 2026
Orchestration is More than Scheduling: Declarative Automation in Dagster
Define the outcome, not the orchestration. Declarative Automation lets you express your desired asset state while Dagster continuously handles the work needed to achieve it.
Blog
July 30, 2026
Community Showcase Part 3
Some of the most interesting Dagster projects come from the community. This post highlights creative community-built applications.
Blog
July 16, 2026
Classifying a Million Snowflake Columns in 9 Days, Solo, with Dagster
data governance. I built a tiered AI classification system, human review workflow, and the Dagster orchestration that ties it all together in production in nine days.
Case study
August 4, 2026
Flo Energy's Data Platform for Critical Energy Data
Flo Energy transformed meter, weather, market, and strategy data into a unified, observable platform with Dagster.
Case study
February 25, 2026
How Magenta Telekom Built the Unsinkable Data Platform
Magenta Telekom rebuilt its data infrastructure from the ground up with Dagster, cutting developer onboarding from months to a single day and eliminating the shadow IT and manual workflows that had long slowed the business down.
Case study
November 25, 2025
Scaling FinTech: How smava achieved zero downtime with Dagster
smava achieved zero downtime and automated the generation of over 1,000 dbt models by migrating to Dagster's, eliminating maintenance overhead and reducing developer onboarding from weeks to 15 minutes.
Guide
January 15, 2026
Modernize Your Data Platform for the Age of AI
While 75% of enterprises experiment with AI, traditional data platforms are becoming the biggest bottleneck. Learn how to build a unified control plane that enables AI-driven development, reduces pipeline failures, and cuts complexity.
Guide
November 5, 2025
Download the eBook on How to Scale Data Teams
From a solo data practitioner to an enterprise-wide platform, learn how to build systems that scale with clarity, reliability, and confidence.
Guide
February 21, 2025
Download the eBook Primer on How to Build Data Platforms
Learn the fundamental concepts to build a data platform in your organization; covering common design patterns for data ingestion and transformation, data modeling strategies, and data quality tips.
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Course
March 19, 2026
AI Driven Data Engineering
Learn how to build Dagster applications faster using AI-driven workflows. You'll use Dagster's AI tools and skills to scaffold pipelines, write quality code, and ship data products with confidence while still learning the fundamentals.
Course
July 11, 2025
Dagster & ETL
Learn how to ingest data to power your assets. You’ll build custom pipelines and see how to use Embedded ETL and Dagster Components to build out your data platform.
