Test and observe

Make data quality automatic

Proactively test and continuously integrate your code so it’s ready for primetime. Squash unexpected bugs fast with detailed breadcrumbs.

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Data quality shouldn’t be hard

dbt Cloud offers proactive ways to test and deploy hardened code and makes building high quality data pipelines an ingrained part of the development workflow. If an issue does arise, you can lean on built-in observability features like column-level lineage, alerts, and logs to trace and resolve issues before trust in your data is broken.

Data tests

Build proactive tests

Proactively define assertions for how you expect your models to behave (column isn’t null, is unique, etc.) or validate the logic within your model — before ever materializing that model in production. Just run "dbt test" and have the confidence that your data products are solid, without driving up your compute bill.

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Continuous integration

Validate code before merging into production

Embrace CI to test code and catch breaking changes or unexpected behavior before new data is delivered to your stakeholders:

Quickly grok the expected impact of a pull request with a summary of the changes directly in Git
Deferral ensures that only changed models are built so you’re not unnecessarily driving up compute
Linting in CI keeps your code consistent

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Monitoring & alerts

Spot, troubleshoot, and resolve issues fast

Observe run history to identify long-running or broken models. Use column-level lineage and audit logs to pinpoint root cause fast. And with real-time alerts in your preferred channel — whether that’s Slack, email, or PagerDuty — you can keep pipelines humming before your stakeholders are the wiser.

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"Column-level lineage in dbt Explorer streamlines root-cause and impact analysis. Rather than painstakingly tracing a column forward or backward in our lineage graph, we’re now able to follow it up and downstream. As a result, we’ll be able to troubleshoot issues more quickly, and develop a more accurate understanding of potential data model changes. "
Katie ClaiborneStaff Analytics Engineer, Cityblock Health
"dbt tests add a ton of value with the clarity and quality they enable. With one or two lines of code, I can make sure my data is quality before it even gets to the stakeholder, which is huge."
Jared StoutHead of Data Management, Sunrun
"dbt makes testing easy, and as a result, people write more tests. Catching data quality issues before your users helps the culture of your team. No one likes to have fingers pointed at them. It’s an overlooked benefit of dbt–what it can do for morale."
Ben SingletonDirector of Data Science & Analytics, JetBlue
"Now that we have an end-to-end view of our data models, we can catch problems before they reach a business user and we hear complaints. These business users now have a much greater trust in the integrity of our data."
Nana EssumanSenior Director of Data Engineering & Data Warehouse, Condé Nast
"One thing I used to hear a lot was 'can I trust this data?' Now everyone knows: if it's there in the warehouse, you can trust it because it's been tested and centralized. and we’ve done a lot of work to communicate our data quality."
Gabriel MarinhoLead Analytics Engineer, Inventa

Want to learn more?

Check out these resources to find out how dbt Cloud can help your teams make data quality automatic.

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