A data pipeline can complete successfully while still delivering incorrect data.
That is why validating only whether an ETL job completed isn't enough.
Effective data testing verifies whether information moved from the source to the target completely, accurately and according to the intended transformation rules.
1. Validate record counts
Start by comparing source and target record counts. Differences may reveal:
- Missing records
- Duplicate records
- Incorrect filtering
- Failed transformations
- Unexpected joins
However, matching record counts alone do not prove that the data is correct.
2. Validate data types
Confirm that target fields contain the expected:
- Data types
- Lengths
- Precision
- Scale
- Date formats
- Nullability
A transformation may technically succeed while truncating or incorrectly converting information.
3. Validate NULL values
Check whether unexpected NULL values have appeared. Questions should include:
- Was the source NULL?
- Should the transformation have supplied a default?
- Is the target field allowed to contain NULL?
4. Validate transformation rules
If the ETL process transforms data, test the actual business logic. Examples include:
- Currency calculations
- Date conversions
- Aggregations
- Conditional transformations
- Code mappings
- Calculated fields
5. Check for duplicates
Validate columns or combinations of columns that should be unique.
Duplicate records can distort reporting and analytics even when every ETL job reports success.
6. Validate referential integrity
Confirm relationships between tables remain valid after loading.
For example, every order associated with a customer should reference an appropriate customer record.
7. Validate incremental loads
Incremental processing deserves special attention. Test:
- New records
- Updated records
- Deleted records
- Unchanged records
- Late-arriving data
8. Reconcile critical business values
Don't stop at row-level comparison. Compare business totals such as:
- Total sales
- Transaction amounts
- Account balances
- Inventory quantities
- Customer counts
Reconciliation provides another layer of confidence that the pipeline is producing meaningful results.
Build data quality into the pipeline
Data validation becomes substantially more valuable when repeatable checks are automated and incorporated into the delivery pipeline.
The goal isn't simply: did the data move? The real question is:
Can the business trust the data that arrived?
At Qanovix, we help organizations validate databases, ETL processes, data migrations and modern data platforms using structured source-to-target testing and repeatable validation.