Both ETL testing and database testing involve data validation, but they are not the same. ETL testing is normally performed on data in a data warehouse system, whereas database testing is commonly performed on transactional systems where the data comes from different applications into the transactional database.
Here, we have highlighted the major differences between ETL testing and Database testing.
ETL testing involves the following operations −
Validation of data movement from the source to the target system.
Verification of data count in the source and the target system.
Verifying data extraction, transformation as per requirement and expectation.
Verifying if table relations − joins and keys − are preserved during the transformation.
Common ETL testing tools include QuerySurge, Informatica, etc.
Database testing stresses more on data accuracy, correctness of data and valid values. It involves the following operations −
Verifying if primary and foreign keys are maintained.
Verifying if the columns in a table have valid data values.
Verifying data accuracy in columns. Example − Number of months column shouldn’t have a value greater than 12.
Verifying missing data in columns. Check if there are null columns which actually should have a valid value.
Common database testing tools include Selenium, QTP, etc.
The following table captures the key features of Database and ETL testing and their comparison −
|Function||Database Testing||ETL Testing|
|Primary Goal||Data validation and Integration||Data Extraction, Transform and Loading for BI Reporting|
|Applicable System||Transactional system where business flow occurs||System containing historical data and not in business flow environment|
|Common tools||QTP, Selenium, etc.||QuerySurge, Informatica, etc.|
|Business Need||It is used to integrate data from multiple applications, Severe impact.||It is used for Analytical Reporting, information and forecasting.|
|Database Type||It is normally used in OLTP systems||It is applied to OLAP systems|
|Data Type||Normalized data with more joins||De-normalized data with less joins, more indexes, and aggregations.|