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.
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.
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