Bad data = bad decisions

Don't let data quality be an afterthought, run quality checks right alongside your data piplelines.

Dagster allows you to build data quality checks in code right where it matters the most. Use native Python, ensure freshness or leverage integrations with Great Expectation for data you can finally count on.

Get better data quality without losing your cool

Integrated data quality checks, without the toll

Adding real-time data quality checks to your existing pipelines is as simple as adding one line of code.



No more chasing downstream errors inside scattered dashboards.

Integrate with best-of-breed tools

With Dagster, you can either write your own data quality checks in Python, or integrate with data quality tools, like dbt tests, Soda, and Great Expectations.

Teams can now define data expectations once, and reuse that across pipelines.

From ingestion to destination and everything in between

Do what no other orchestrator does and leave your competitors in the dust.

Attach data quality tests to the data assets you care about source systems, through transformations, and all the way to your reporting layer and beyond.

Catch problems before they hit production

No more stakeholder surprises

Enforce data quality checks & rules right inside Dagster, preventing bad data from spilling into other data assets.

Stop drowning in false alerts and noise

Dagster ties validations to lineage, so when something fails, you don’t just get an alert, you get context.

Fix data quality issues before your team notices

Catch schema mismatches, unexpected nulls and more, so you can finally trust every pipeline run.

Start your data journey today

Unlock the power of data orchestration with our demo or explore the open-source version.

Try Dagster+

Data quality shouldn’t be a separate workflow.

Dagster lets you define and run data quality checks where your data lives—alongside your pipelines. No separate tools, no disconnected alerting.

Define, trigger, and monitor checks — all in one place

Whether you’re checking freshness, row counts, or nulls, Dagster lets you run checks inside your pipelines or on a schedule.

Use the UI to see where checks are defined, how they’re triggered, and which assets they apply to. No jumping between systems.

See the full picture, instantly

Checks are visible across your entire DAG. 



If a single upstream asset fails a check, you’ll see the impact downstream—so issues never go unnoticed. 



And because everything’s code-defined, it's easy to enforce data quality standards on all pipelines.

Track issues to the exact asset, owner, and cause

You get fine-grained visibility into every failure.

See which check failed, on which asset, and who owns it, without digging through logs or asking around. It's built for clarity, not chaos.

From alerts to action

Alerts aren’t helpful if they just say tell you that something broke.

Dagster notifies you the moment something fails, along with where, and what it affects—allowing teams to go straight to the root cause.

“The main benefit is that Dagster provides a foundational abstraction for building a reliable, observable, and composable data platform.”
Tobias Macey
Host of the Data Engineering Podcast & Al Engineering Podcast

Latest writings

The latest news, technologies, and resources from our team.

dbt Fusion Support Comes to Dagster

August 22, 2025

dbt Fusion Support Comes to Dagster

Learn how to use the beta dbt Fusion engine in your Dagster pipelines, and the technical details of how support was added

What CoPilot Won’t Teach You About Python (Part 2)

August 20, 2025

What CoPilot Won’t Teach You About Python (Part 2)

Explore another set of powerful yet overlooked Python features—from overload and cached_property to contextvars and ExitStack

Dagster’s MCP Server

August 8, 2025

Dagster’s MCP Server

We are announcing the release of our MCP server, enabling AI assistants like Cursor to seamlessly integrate with Dagster projects through Model Context Protocol, unlocking composable workflows across your entire data stack.