The Three Types of Data Validation
The three core types of data validation are syntax validation, integrity validation, and business-rule validation. Advanced ETL Processor applies these checks inside ETL workflows, so format errors, broken relationships, and logic violations are caught before bad data reaches reporting or operations.
If your team keeps finding issues after data is loaded, validation is probably too late or too shallow. The simplest approach is to validate at each step in the workflow using a clear data validation process, so problems are blocked early instead of discovered in a monthly report.
1) Syntax validation checks format and structure
Syntax rules confirm values match required patterns: date format, email pattern, numeric shape, field length, and allowed characters. This is your first filter for obvious bad input.
Examples include validating ISO dates, rejecting malformed IDs, and enforcing required fields. It is basic, but skipping it is how garbage enters quickly.
2) Integrity validation checks relationships and consistency
Integrity validation verifies that relationships hold across records and systems. Typical checks include foreign-key consistency, duplicate key detection, and cross-table alignment.
Reference guidance from IBM and Built In highlights this as essential for trustworthy analytics.
3) Business-rule validation checks real-world logic
Business rules test whether data makes sense in context. For example, order date cannot be after shipment date, quantity cannot be negative, and status transitions must follow policy.
These rules are where technical validation meets operational reality, and they usually prevent the most expensive downstream mistakes.
Two often-missed checks: range drift and reference snapshots
Range drift checks catch values that remain technically valid but become suspicious over time. Reference snapshots preserve lookup tables so validation results stay reproducible during audits.
Rule of thumb: if a value passes format checks but fails common sense, add a threshold or historical comparison rule.
One support story to keep in mind
A customer had intermittent load failures that looked random. The cause was one source row with text in a numeric field. After adding syntax and business-rule checks before load, the failures stopped. One rogue value can waste a full run if validation is only done at the end.