Top 11 Essential Considerations for Performing ETL Testing

ETL testing is a crucial part of data warehouse systems. It involves performing end-to-end testing of a data warehouse application. It ensures that data is accurately extracted, transformed, and loaded into the target system. In this blog, we delve into the significance of ETL testing and its role in maintaining data integrity. Let’s unravel the key components and best practices for conducting effective ETL tests.

Fig: Top 11 Essential Considerations for Performing ETL Testing

1. Requirements Testing

The objective of requirements testing is to ensure that all defined business requirements are as per the business user’s expectations. During requirements testing, the testing team should perform the analysis of business requirements in terms of requirement test ability and completeness. The following pointers should be considered during requirement testing:

• Verification of logical data model with design documents
• Verification of Many – Many attribute relationship
• Verification of the type of keys used
• All transformation rules must be clearly specified.
• Target data type must be specified in data model or design document.
• Purpose and overview of the reports must be clearly specified.
• Report design should be available.
• All report details such as grouping, parameters to be used, and filters should be specified.
• Technical definitions such as data definitions and details about the tables and fields would be used in reports.
• All details for header, footer, and column heading must be clearly specified.
• Data sources and parameter name and value must be clearly specified.
• Verification of technical mapping in terms of report name, table name, column name and description of each report must be documented.

Generative AI in requirements testing can help generate diverse test cases automatically, improving software testing efficiency.

2. Data Model Testing

The objective of this testing is to ensure that the physical model is in accordance with the logical data model. The following activities should be performed during this testing:

• Verification of logical data model as per design documents.
• Verification of all the entity relationships as mentioned in the design document.
• All the attributes and keys must be defined clearly.
• Ensure that the model captures all requirements.
• Ensure that the design and actual physical model are in sync.
• Ensure naming conventions.
• Perform schema verification.
• Ensure that the table structure, keys, and relationship are implemented in the physical model as per the logical model.
• Validation of Indexes and Partitioning

Generative AI in data model testing can simulate realistic data scenarios, improving the software’s ability to handle diverse inputs effectively.

3. Unit Testing

The objective of Unit testing is to validate whether the implemented component is functioning as per design specifications and business requirements. It involves testing of business transformation rules, error conditions, mapping fields at staging and core levels. The following pointers should be considered during Unit Testing:

• All transformation logic should work as designed from source till the target.
• Surrogate keys have been generated properly.
• NULL values have been populated where expected.
• Rejects have occurred where expected and a log for rejects is created with sufficient details.
• Auditing is done properly.
• All source data that is expected to be loaded into the target is loaded− compare counts between the source and the target.
• All fields are loaded with full contents− i.e. no data field is truncated while transforming.

Generative AI in unit testing can automate the generation of test scenarios, improving test coverage and efficiency in the software development process.

4. System Integration Testing

Once unit testing is done and all exit criteria of unit testing are met, the next phase of testing is integration testing. The objective of integration testing is to ensure that all integrated components are working as expected. The data warehouse application must be compatible with upstream and downstream flows and all the ETL components should be executed with correct schedule and dependency. The following pointers should be considered during Integration Testing:

• ETL packages with Initial Load
• ETL packages with Incremental Load
• Executing ETL packages in sequential manner
• Handling of rejected records
• Exception handling verification
• Error logging

Generative AI can be utilized in system integration testing for software to automatically generate test cases.

5. Data Validation Testing

The objective of this testing is to ensure that the data flow through the ETL phase is correct and cleansed as per the applied business rules. The following listed pointers should be considered during Data Validation Testing:

• Data comparison between source and target
• Data flow as per business logic
• Data type mismatch
• Source to target row count validation
• Data duplication
• Data correctness
• Data completeness

Generative AI in data validation testing for software can simulate a wide range of scenarios, aiding in uncovering edge cases and potential vulnerabilities.

6. Security Testing

The objective of this testing is to ensure that only an authorized user can access the reports as per assigned privileges. While performing security testing, the following aspects should be considered:

• Unauthorized user access
• Role based access to the reports

Generative AI can be used in security testing to simulate various attack scenarios, helping software developers identify vulnerabilities more effectively.

7. Report Testing

The objective of report testing is to ensure that BI reports meet all the functional requirements defined in the business requirement document. While performing functional testing, the following aspects should be considered:

• Report drill down, drill up, and drill through
• Report navigation and embedded links
• Filters
• Sorting
• Export functionality
• Report dashboard
• Dependent reports

Verify the report runs with a broad variety of parameter values and in whatever way the users will be receiving the report (e.g. A subscription runs and deploys the report as desired.)

• Verify that the expected data is returned.
• Verify that the performance of the report is within an acceptable range.
• Report data validation (Correctness, Completeness, and integrity).
• Verify required security implementation.
• Automating processes whenever possible will save tremendous amounts of time.
• Verify that the business rules have been met.

Generative AI in report testing allows for the creation of realistic synthetic data, aiding in comprehensive testing scenarios.

8. Regression Testing

The objective of regression testing is to keep the existing functionality intact each time new code is developed for a new feature implementation or if existing code is changed during correction of application defects. Prior to regression testing, impact analysis must be carried out in coordination with developers in order to determine the impacted functional areas of application.

Ideally, 100% regression is recommended for each drop/build. In case builds are too frequent and there is a time limitation on test execution, the regression should be planned for execution based on the priority of test cases.

Generative AI in regression testing can automatically create new test cases to cover a wide range of scenarios, enhancing the thoroughness of software testing processes.

9. Performance Testing

The objective of performance testing is to ensure that reports or data on the reports are loaded as per the defined nonfunctional requirements. In performance testing, different types of tests would be conducted such as load test, stress test, volume test etc. While executing performance testing, the following aspects should be considered:

• Compare the SQL query execution time on Report UI and backend data.
• Check concurrent access to the reports with multiple users.
• Report rendering with multiple filters applied.
• Load the high volume of production-like data to check the ETL process and check whether the ETL process does it in an expected timeframe.
• Validate the OLAP system performance by browsing the cube with multiple options.
• Analyze the maximum users load at peak and off-peak times that can access and process BI reports.

Generative AI in performance testing can simulate a wide range of scenarios, helping software teams identify and address potential performance issues before deployment.

10. Test Data Generation

As test data is very important, in ETL testing, appropriate test data needs to be generated. So, depending on the volume of data, test data will be generated and used by using a test data generation tool or SQL scripts. As a best practice, generated test data would be similar to production like data.

Data masking for test data generation – Data masking is the process of protecting personal sensitive information. Data is scrambled in such a way that sensitive information can be hidden but still usable for testing without being exposed. A few data masking techniques are:

• Randomization: Generate random data within the specified data range
• Substitution: The data presented in columns will be replaced completely or partially with artificial records.
• Scrambled: The data type and size of the fields will be intact, but the records will be scrambled.

Generative AI in test data generation for software helps create diverse and realistic datasets efficiently, enhancing the quality of software testing.

11. User Acceptance Testing

The objective of UAT testing is to ensure that all business requirements or rules are met from the business user perspective, and the system is acceptable to the customer.

User Acceptance Testing (UAT) ensures that the ETL process meets the end user’s requirements and expectations. By involving end users in testing, any issues related to data accuracy, completeness, or transformation can be identified and resolved early on. This collaboration helps in delivering a reliable and user-friendly ETL solution that aligns with the business needs.

Generative AI in user acceptance testing can help create diverse scenarios quickly, improving software robustness.

There you have it – our top 11 essential considerations for performing ETL testing. We are sure these will be of help to you as you navigate your ETL testing journey henceforth.

Write to us with your thoughts about ETL testing and visit us at Nitor Infotech if you’d like to learn more about our services.