Automate the data testing of your Data Warehouse to accelerate testing cycles, reduce costs & risks and improve data quality.
How we do Data Warehouse Testing
According to authors Doug Vucevic and Wayne Yaddow in the book "Testing the Data Warehouse Praticum" (Trafford Publishing), some of the main challenges to test for in data warehouse testing are:
- Data Completeness
- Data Transformation
- Data Quality
- Regression Testing
The only way to perform these tests in a reasonable time frame, which will compare huge volumes of data, is through automating the tests.
The 3 Biggest Issues with Data Warehouse Testing
- The #1 Method to compare data from sources and target data warehouse – Sampling, also known as “Stare and Compare” - is an attempt to verify data dumped into Excel spreadsheets by viewing or “eyeballing” the data. Less than 10% is usually verified and reporting is manual.
- The #2 Method – MINUS queries – subtracts data sets from each other twice and you analyze leftover rows - is inefficient and produces no audit trail or reporting.
- Both methods require SQL programming and very few testers, analysts or operations people know SQL.
Data migration Testing: Easily Validate & Test the Data Migration Process
Migrating data has become one of the most challenging initiatives for IT managers. Although these projects yield high business benefits (such as cost savings, increased productivity, and improved data manageability), they tend to involve a high level of risk due to the volume and criticality of the data.
In order to reduce risk and ensure that the data has been migrated and transformed, you need to implement a thorough validation and QA strategy. DvT helps you test your data quickly and easily.
Testing the Transformations (ETL)
For tables with transformations, you can create Table Pairs - one aimed at the existing system and one at the new system. You can run queries any time, by scheduling them for a particular time & date or after an event. Reports can then be automatically sent to your team. Learn about everything you can accomplish.
Supported Data Sources
DvT supports databases, data marts, data warehouses, Hadoop, and flat files as either sources or targets.