The objective of ETL Testing is to test the ETL process in order to ensure that data is effectively managed in the Data Warehouse.
The ETL testing identifies the problems, discrepancies with the data source, as well as uncertain business rules applied in the integration. To overcome this, the below process is followed,
- Data Mapping or Transformation: Verify that data is transformed correctly according to various business requirements and rules.
- Source to Target count: Make sure that the count of records loaded in the target is matching with the expected count.
- Source to Target Data: Make sure that all projected data is loaded into the data warehouse without any data loss and truncation.
- Data Quality: Make sure that ETL application appropriately rejects, replaces with default values and reports invalid data.
- Performance: Make sure that data is loaded in a data warehouse within prescribed and expected time frames to confirm improved performance and scalability.
- Production Validation: Validate the data in the production system & compare it against the source data.
- Data Integration: Make sure that the data from various sources has been loaded properly to the target system and all the threshold values are checked.
- Application Migration: In this testing, it is ensured that the ETL application is working fine on moving to a new box or platform.
- Data & Constraint: The datatype, length, index, constraints, etc. are tested in this case.
- Duplicate Data Check: Test if there is any duplicate data present in the target systems. Duplicate data can lead to wrong analytical reports.
- End-User Testing: It involves generating reports for end-users to verify if the data in the reports are as per expectation. It involves finding deviation in reports and cross-check the data in the target system for report validation.
- Retesting: It involves fixing the bugs and defects in data in the target system and running the reports again for data validation.
To recognize the difficulties early in the ETL process can prevent expensive delays and hindrances.
Following are the importance of ETL testing,
- Helps in identifying problems with the data source,
- Prevents loss of data and data duplication,
- Eliminates possible errors in the transmission,
- Facilitates the Transfer of Bulk Data.
Challenges while performing testing,
- Incorrect, incomplete or duplicate data.
- Data loss during the ETL process.
- The data warehouse system contains historical data, so the data volume is too large and extremely complex to perform ETL testing in the target system.
- Due to the high volume of data, the test scripts take more time to execute.
- ETL testing involves various complex SQL concepts for data validation in the target system.
- Unstable testing environment
- Handling special characters in the target system
- Missing business flow information