What are validation done in ETL testing process?

In ETL (Extract, Transform, Load) testing, validation is a crucial step to ensure the accuracy, completeness, and reliability of the data transformation and loading processes. The validation process involves checking various aspects of the data at different stages of the ETL pipeline. Here are some common types of validations performed in ETL testing:

1. Schema Validation:

   • Ensure that the data extracted from the source system adheres to the expected schema.
   • Validate the schema of the transformed data to ensure it matches the target schema.

2. Data Completeness:

   • Check whether all expected records from the source system are present in the extracted data.
   • Ensure that no records are lost during the transformation process.

3. Data Accuracy:

   • Compare the values in the target system with the expected values to ensure accuracy.
   • Check for data integrity and verify that the transformed data meets business rules and requirements.

4. Data Transformation Validation:

   • Validate that the transformations applied to the data are correct and meet the specified business rules.
   • Check aggregations, calculations, and derivations to ensure accuracy.

5. Data Consistency:

   • Ensure consistency between related data fields and tables in the target system.
   • Validate foreign key relationships and referential integrity.

6. Duplicate Data Check:

   • Identify and handle duplicate records that might have been introduced during the ETL process.
   • Check for duplicate keys and values in target tables.

7. Null Value Checks:

   • Verify that mandatory fields do not contain null values unless explicitly allowed.
   • Check for unexpected null values in the transformed data.

8. Concurrency and Parallelism:

   • Validate the ETL process's ability to handle multiple transactions and concurrent loads.
   • Ensure that parallel processing does not result in data corruption or inconsistencies.

9. Performance and Scalability:

   • Validate the performance of the ETL process by assessing its speed and efficiency.
   • Check whether the ETL process scales appropriately as the volume of data increases.

10. Metadata Validation:

    • Verify that metadata (data about the data) is accurately captured and stored.
    • Check if the metadata is consistent with the actual data in the target system.

11. Error Handling and Logging:

    • Validate the effectiveness of error handling mechanisms.
    • Review error logs to identify and address issues during the ETL process.

12. Historical Data Validation:

    • If dealing with historical data, ensure that historical changes are accurately captured and represented in the target system.

13. Reconciliation:

    • Perform reconciliation between the source and target systems to verify that the data in the target system matches the source system.

14. Regression Testing:

    • Perform regression testing to ensure that changes or enhancements to the ETL process do not introduce new issues or break existing functionality.

These validation checks collectively help ensure the quality and reliability of the data as it moves through the ETL pipeline, ultimately supporting informed decision-making based on accurate and trustworthy information.