Data testing ensures the accuracy, reliability, and integrity of data. It validates correctness, completeness, consistency, and timeliness. Data testing is crucial for informed decision-making and regulatory compliance. Well tested data is a key for business intelligence, analytics, and reporting.
Scope of Requirement Analysis:
- Identify and document business requirements for data integration and testing.
- Gather input from stakeholders to understand data sources, transformations, and target system expectations.
- Define data quality criteria, validation rules, and acceptance criteria for testing.
- Document data mappings, transformations, and business rules to guide testing efforts.
- Ensure alignment between business requirements and technical implementation.
Scope of Documentation Analysis:
- Review existing documentation, such as data dictionaries, data models, and ETL specifications.
- Analyze data source schemas, formats, and metadata to understand data structure and semantics.
- Evaluate data transformation logic, transformation rules, and mappings to target schemas.
- Identify potential gaps, ambiguities, or inconsistencies in documentation that may impact testing.
- Update or enhance documentation as needed to reflect changes, requirements, or insights from testing efforts.
Below list is applicable for all the steps that testing can be divided at: Source to Staging area, Staging to Target area, etc.
- Data availability: source data that is used in testing should cover all the possible use cases of data filtration/transformation.
- If the above is not met, test data should be mocked up.
- Data filtration rules implementation.
- Data modification rules implementation.
- Data integrity: data transformed/loaded via tested functionalities should not break data integrity of existing system.
- Appearance of unexpected data spikes/duplicates/any other anomalies.
- Designed tests should be created considering the possibility of reuse/easy maintenance.
In summary, the main difference between ETL and ELT testing lies in the timing and location of data transformation: ETL testing focuses on validating transformations before data loading, while ELT testing focuses on validating transformations after data loading. Both approaches are essential for ensuring the accuracy, reliability, and integrity of data integration processes.
Approaches to test error handling functionalities depend on a particular implementation of error handling, but in general are focused on:
- Evaluate error detection and logging mechanisms.
- Test error recovery and rollback procedures.
- Verify error messages and notifications.
- Measure data loading and transformation speeds.
- Assess system performance under varying data volumes.
- Test scalability of ETL/ELT processes.
Regression testing is conducted when there's a need to ensure that existing functionality remains intact. Main points here are:
- Create and maintain a regression tests library (usually it is based on functional tests for previously implemented functionalities + additions that can cover basics)
- Validate data consistency and integrity post-updates.
- Test backward compatibility with existing data and systems.
- Establish testing environments that mirror production setups.
- Ensure availability of test data sets for different scenarios.
- Consider cloud-based environments for scalability and flexibility.