Data warehouse & ETL testing
Bad data leads to bad business decisions. We test your ETL so every record arrives complete, correct and on time.
The challenges this solves
We don't trust our reports
Numbers in the warehouse don't always match the source systems.
Loads keep getting slower
As data volumes grow, ETL load times and queries slow down.
Defects surface too late
We find data problems after go-live, when they cost far more to fix.
What we deliver
- Data completeness: record counts, unique keys and full field contents from source to warehouse
- Data transformation: automated profiling and data movement validation against expected results
- Validation of ETL-generated fields such as surrogate keys, data types and referential integrity
- Data quality: handling of rejected, replaced, corrected and reported records
- Scalability and performance at maximum expected production volumes
- Integration testing with upstream and downstream processes, using production-like data
- User acceptance and regression testing of ETL changes
4 steps, clear deliverables
- 01
Define scenarios
We build input scenarios and expected results, and validate them with your business users.
You receive: Test scenario spreadsheet
- 02
Validate the data
We test completeness, transformation and data quality rules with automated profiling.
You receive: Data validation results and invalid-data reports
- 03
Test at scale
We load maximum expected volumes and time each ETL component to find weak points.
You receive: Load-time and query performance findings
- 04
Test end to end
We run the full process in production order, with the same dependencies.
You receive: Integration test results
Want to know what this would take for your release? Start with a short scoping call.
Who we do this for
- Financial services
- Retail & e-commerce
- Healthcare
- Technology
Related services
Questions we hear most
What do you test in an ETL process?
We test data completeness, transformation, data quality, scalability and performance, integration, user acceptance and regression.
How do you test complex transformation logic?
Spot-checking samples is slow. We combine automated data profiling with automated data movement validation.
Do you use real production data?
We use production-like data and randomize sensitive fields where privacy or security requires it.
Which industries does this approach come from?
Our ETL testing approach draws on data warehouse work in financial services and consumer retail.
Make your warehouse data trustworthy
Tell us about your sources, ETL tools and load windows. We will plan tests for each risk area.