Data and ETL testing you can reconcile to the source
When a dashboard number is wrong, people stop trusting the dashboard. We validate data from source systems through every transformation to the warehouse, so your reporting holds up to scrutiny.
Bad data rarely announces itself
Data defects do not crash an application. They quietly flow into reports, forecasts and customer communications until someone notices a number that does not add up.
- Silent transformation errorsJoin, filter and mapping mistakes drop or duplicate records without any error.
- Risky migrationsMoving to a new platform without full reconciliation leaves gaps nobody can see.
- Manual spot checksSampling a few rows by hand cannot prove millions of rows are correct.
- Unclear ownershipNobody is sure which system is the source of truth for a given field.
Evidence that the data is right
We translate mapping documents and business rules into executable SQL validations: row counts, completeness, uniqueness, referential integrity, transformation logic and reconciliation of totals between source and target.
Those checks become repeatable, so every load and every migration cycle is measured against the same standard rather than re-checked by hand.
Capabilities
Database testing
Schema, constraint, stored procedure and data integrity verification.
ETL testing
Validation of extraction, transformation rules and load behaviour, including incremental loads.
Source-to-target validation
Field-level checks that target data matches the source after mapping rules are applied.
Data migration testing
Full reconciliation across migration dry runs and the final cut-over.
SQL validation
Review and testing of the queries behind reports, views and transformations.
Data reconciliation
Count, sum and balance checks that prove nothing was lost or double-counted.
Databricks testing
Validation of notebooks, Delta tables and pipeline outputs on Databricks.
Data warehouse validation
Dimension, fact and aggregation checks across warehouse layers.
How we work
Understand the data
Review sources, mappings, business rules and the reports that depend on them.
Define rules
Turn requirements into measurable validation rules and tolerances.
Build validations
Write reusable SQL and scripted checks for each layer of the pipeline.
Reconcile & report
Run checks, investigate differences and document findings clearly.
Automate
Schedule validations to run with each load so issues surface immediately.
What it means for your business
Technologies we use
- SQL
- SQL Server
- PostgreSQL
- Databricks
- Python
- Azure DevOps
- Git
- CI/CD
Trusted reporting
Leaders can act on dashboards without second-guessing the numbers.
Safer migrations
Cut-over decisions are based on full reconciliation, not samples.
Faster root cause
When numbers differ, validation results point to the layer where it happened.
Repeatable assurance
The same checks run on every load, not only during projects.
Frequently asked questions
What is source-to-target validation?
It is the practice of confirming that each target field contains exactly what the source data should produce after the documented mapping and transformation rules are applied.
Can you test large data volumes?
Yes. We favour set-based SQL comparisons and aggregate reconciliation over row-by-row sampling, which scales to large tables.
Do you need access to production data?
Not necessarily. We can work with masked or representative data and agree access rules with your security team before we start.
Do you test Databricks pipelines?
Yes. We validate notebook logic, Delta table outputs and data quality across bronze, silver and gold layers where that pattern is used.
From our Insights
A Practical Checklist for Source-to-Target Data Validation
A pipeline can complete successfully and still deliver incorrect data. Eight checks that prove it arrived complete and correct.
Related services
Talk to Qanovix about data & ETL quality
Tell us where you are today and what you need to achieve. We will recommend a practical next step, even if it is small.