​
Introduction

Data validation lets you define rules that detect anomalies or unexpected results after a workflow run. Use it to detect missing values, outliers, and schema issues before downstream use.

​
Prerequisites

  • A completed workflow run with preview data

​
Configure validation

  1. Open the workflow in your dashboard.
  2. In the sidebar, select Issues. You will see 3 tabs there:
    • Key columns: Define how rows are tracked across runs.
    • Rules: Create, view, suggest, and delete validation rules.
    • Results: Review issues found by rules for the selected run.
  3. Select the Rules tab.

Issues Rules sidebar navigation

  1. Choose one of the following:
    • Add new Rule: Manually choose a target column, define a condition, and (optionally) add domain hints for better precision.
    • Suggest Rules: Kadoa auto-suggests rules from your schema and sample data.

Rule creation dialog

Manual rules often provide the most precise results when you know the domain.

​
Rule execution

  • Rules run at the end of each subsequent pipeline run.
  • Adding or removing a rule takes effect on the next run.
  • If data shape changes make a rule invalid, Kadoa disables it automatically.

​
Working with rules

  • Add rule: Select target columns and describe the rule in natural language. Kadoa generates the rule.
  • Suggest rules: Auto-generate common rules based on data types.
  • Delete rules: Remove a single rule or delete all rules.
  • Disabled rules: Rules can be auto-disabled when they no longer apply. A reason is shown when available.
  • Raw rule code: Rules expose raw SQL for transparency.

​
Example rules

Here are examples of natural language inputs and their generated SQL validation 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%'

-- Natural language: "Check that publication dates are not in the future"
WHERE publication_date > CURRENT_DATE()

Raw SQL view for a rule

  • Historical runs: When viewing a past run, you’ll see the rules that were in effect at that time (read-only).

​
Validation report

After a run finishes, go to Issues → Report.
  • See issues grouped by rule.
  • Click an issue to open the row detail and view all issues associated with that row.

Validation report overview

​
Results view details

  • Filter by rule: Use the filter to focus on specific rules when multiple are present.
  • Row details: Click an item to open the row and see the offending value and related context.

Row detail view with issues

  • Status indicators:
    • NEW: first time the issue appears
    • RESOLVED: issue no longer present
  • Summary chips show change since previous run:
    • +n new issues, –n resolved

​
Key columns (optional)

Defining key columns lets Kadoa track rows across runs for richer insights.
  • Configure in Issues → Key columns.
  • Pick one or more columns used to match the same row across runs.
  • Requirements: values should be present for most rows and unique per row (no duplicates).

Key columns configuration

​
How to pick key columns

  • Prefer stable identifiers (e.g., product ID, URL, SKU).
  • If a row cannot be matched via the key, it is treated as a new row.

​
Key‑based insights

When key columns are set, the report shows change indicators between runs:
  • +n: new issues discovered since the previous run
  • -n: issues resolved since the previous run
Individual issues are labeled as “new” or “resolved” when applicable.

​
Validate now

Use the Validate now button to schedule validation for the current workflow’s latest data. This is available when no specific past run is selected.

​
Notifications

Enable the notification “New data validation issues detected” to be alerted when anomalies increase compared to the previous run.