Learn · Demo
Data quality checks
Data-quality checks don't create problems — they find them. Every issue caught here is one that won't corrupt a model, break a dashboard or surprise a stakeholder. Toggle the rules below and watch the checks do their job: the more they surface, the better your pipeline is working.
Validation rules — tap to toggle:
Issues caught
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Checks run
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Records flagged
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Rules active
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■ highlighted cell = issue caught by an active rule. Hover for the reason. Each highlight is the system doing its job.
Why catching errors is a success
A data-quality check that surfaces a problem hasn't "failed" — it's done exactly what it's supposed to. A missing value, a malformed email, a duplicate ID or an out-of-range figure left unchecked will quietly corrupt analytics, train biased models and break downstream pipelines. The checks are your safety net: the more issues they catch, the more trust you can place in everything that passes through. Zero issues caught with no checks active doesn't mean your data is clean — it means no one is looking.
This is an illustrative sample. In production, rules like these run continuously in the pipeline (e.g. with Great Expectations, dbt tests or Spark jobs), with alerting and quarantine for records that need attention.