1. TestCase Design Criteria
- Performance Metrics : 성능 지표
- Data Quality and Transformation : 데이터 품질 및 변환
- Integration and Compatibility : 통합 및 호환성
- Security and Compilance : 보안 및 규정 준수
- Usability and Support : 사용성 및 지원
2. Performance Metrics
2-1) Data Throughput and Scalability(데이터 처리량 및 확장성)
- Objective: Assess how efficiently the ETL tool processes large volumes of data.
- Metrics: Measure the data throughput in terms of records or MB/GB per second. Evaluate how the tool scales when the volume of data increases.
- Test Scenarios: Load various data volumes to test the tool's scalability. Use datasets of different sizes (small, medium, large) and observe the performance.
2-2) Job Execution Time
- Objective: Determine the time taken to execute ETL jobs from start to finish.
- Metrics: Record the time taken to complete extraction, transformation, and loading processes individually and in combination.
- Test Scenarios: Run ETL jobs with different complexities, such as simple data transfers versus complex transformations.
2-3) Resource Utilization
- Objective: Understand the ETL tool’s consumption of system resources like CPU, memory, and disk I/O.
- Metrics: Monitor resource utilization during ETL processes. Evaluate if the tool is optimized for your environment (on-premises or cloud).
- Test Scenarios: Conduct tests under varying loads to observe how resource usage changes and whether it remains within acceptable limits.
3. Data Quality and Transformation
3-1) Data Accuracy and Consistency
- Objective: Ensure that the data remains accurate and consistent throughout the ETL process.
- Metrics: Measure the accuracy of the transformed data by comparing it with the original dataset. Check for consistency across multiple runs.
- Test Scenarios: Introduce known errors or anomalies in the source data to see if the ETL tool correctly identifies and handles them.
3-2) Error Handling and Logging
- Objective: Evaluate the tool’s ability to handle errors during ETL processes.
- Metrics: Review the error handling mechanisms, such as retries, error logging, and alerts. Measure the tool’s effectiveness in maintaining data integrity.
- Test Scenarios: Simulate common ETL errors (e.g., network failure, data format issues) to test the tool’s response and logging capabilities.
3-3) Transformation Capabilities
- Objective: Assess the tool’s ability to perform complex data transformations.
- Metrics: Measure the complexity and flexibility of transformations that can be implemented. Evaluate the tool’s built-in functions and support for custom transformations.
- Test Scenarios: Implement a variety of transformation rules, including joins, aggregations, and data cleansing operations.
4. Integration and Compatibility
4-1) Source and Target System Compatibility
- Objective: Ensure the ETL tool supports the data sources and target systems you use.
- Metrics: Verify the tool’s ability to connect to and interact with your databases, file systems, APIs, and other data sources/targets.
- Test Scenarios: Test connections to all critical data sources and destinations, including cloud and on-premises systems.
4-2) API and Extensibility
- Objective: Determine how well the ETL tool integrates with other applications and systems.
- Metrics: Evaluate the availability and robustness of APIs, SDKs, and integration points.
- Test Scenarios: Attempt to extend or customize the tool using its API or integrate it with your existing data infrastructure.
4-3) Automation and Workflow Integration
- Objective: Assess the ETL tool’s ability to automate tasks and integrate into existing workflows.
- Metrics: Evaluate the support for automation features like scheduling, job chaining, and event-driven execution.
- Test Scenarios: Set up automated workflows, including triggers and dependencies, to see how seamlessly the tool integrates with your processes.
5. Security and Compliance
5-1) Data Security
- Objective: Ensure that the ETL tool meets your organization's security standards.
- Metrics: Evaluate encryption options for data in transit and at rest, user authentication, and access control mechanisms.
- Test Scenarios: Review security settings and policies within the tool. Test the encryption and access control features.
5-2) Compliance and Governance
- Objective: Determine if the tool supports compliance with relevant data protection regulations (e.g., GDPR, HIPAA).
- Metrics: Check for features like data masking, auditing, and compliance reporting.
- Test Scenarios: Simulate compliance scenarios to ensure the tool can enforce and report on regulatory requirements.
6. Usability and Support
6-1. User Interface and Experience
- Objective: Assess how easy the ETL tool is to use for both technical and non-technical users.
- Metrics: Evaluate the user interface design, intuitiveness, and ease of use. Consider the availability of drag-and-drop features and wizards.
- Test Scenarios: Have a mix of users (developers, analysts, etc.) interact with the tool to provide feedback on usability.
6-2. Documentation and Support
- Objective: Ensure that comprehensive documentation and support resources are available.
- Metrics: Evaluate the quality of user manuals, online resources, and customer support. Consider the availability of training programs.
- Test Scenarios: Attempt to resolve a problem or complete a task using only the provided documentation and support channels.
6-3. Deployment and Maintenance
- Objective: Assess the ease of deployment and ongoing maintenance requirements.
- Metrics: Measure the time and resources required to deploy the tool, as well as the complexity of maintaining it.
- Test Scenarios: Perform a trial deployment in your environment, followed by routine maintenance tasks like updates and backups.
Data Engineer