Define Data Quality Rules before Building a Pipeline

A practical approach

Step 1

List the required fields and relationships for the downstream task. Distinguish a hard rejection from a warning that can be reviewed later.

Step 2

Check quality at ingestion and after transformations. Record counts and reasons for rejected or quarantined records.

Step 3

Assign ownership for recurring failures. Use examples to improve the source process, not only the cleanup script.

Worked example

Workshop registrations arrive from two forms using different topic names. Both imports succeed, but reporting splits the same course into two categories. A controlled mapping and a validation rule can make the intended category consistent while preserving the original source value for investigation.

A mistake to avoid

Automatically filling missing values can make dashboards look healthier while hiding the actual problem. Keep inferred, defaulted and user-provided values distinguishable when that difference matters.

Try it yourself

Take a sample of fifty records and define five quality checks tied to a real workflow. Run the checks and inspect every failure category before deciding what to automate.

Your implementation checklist

• Quality rules serve a named use case.
• Rejected records remain traceable.
• Transformations receive their own checks.
• Recurring source problems have an owner.