Inconsistent data quality is the biggest bottleneck in data workflows. Bad data cascades through pipelines, breaks downstream systems, and erodes trust in automation.

Our new data validation system catches these issues automatically, letting you define quality rules in plain English and monitor data health across every workflow run.

How it works

1. Describe rules naturally: "All prices should be positive numbers" → automatic SQL validation
2. Auto-suggest common patterns: Kadoa suggests rules based on your data types and schema
3. Review validation reports: See which rows fail validation with detailed context

Go to your workflow → Issues → Rules to get started.

Example rules

-- Natural language: "Check email formats are valid"
WHERE email NOT REGEXP '^[A-Za-z0-9+_.-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,}$'

-- Natural language: "All prices should be positive"  
WHERE price <= 0 OR price IS NULL

-- Natural language: "Product URLs should contain the domain"
WHERE url NOT LIKE '%example.com%'

What you'll see

When validation runs, the Results tab shows:

• Issues grouped by rule with severity indicators
• NEW and RESOLVED status tracking across runs
• Row-level details showing exactly what failed

How to use

Data validation runs automatically after each workflow execution. Try it out and catch quality issues before they impact your systems.

You can add rules to any workflow by opening it and navigating to the Issues tab

Read the full documentation →