Parity Testing

Data migration parity testing focuses on verifying data accuracy, completeness, and integrity during migration. It ensures that the data from the legacy system is successfully migrated to the cloud environment and maintains consistency with the original data.

To perform data migration parity testing, follow these steps:

Data Mapping and Analysis

Analyze the legacy system's data structures, formats, and relationships. Understand how the data is stored, organized, and accessed. Identify the mapping between the legacy system data and the modernized application's target data model.

Data Extraction

Extract the data from the legacy system using appropriate extraction mechanisms such as database queries, APIs, or data export utilities. Ensure that the extracted data represents a comprehensive and representative sample of the original dataset.

Data Transformation and Cleansing

Apply necessary data transformations and cleansing procedures to prepare the data for migration. This may involve converting data formats, normalizing or denormalizing data, removing duplicates, or resolving data quality issues.

Data Loading

Load the transformed data into the target environment, such as a cloud-based database or storage system. Ensure the data is loaded wholly and accurately, maintaining the relationships and dependencies between different entities.

Data Verification

Compare the migrated data with the source data from the legacy system to validate its accuracy and completeness. Perform data integrity checks, such as verifying record counts, validating primary key and foreign key relationships, and cross-referencing data between different tables or entities.

Data Consistency Testing

Perform data consistency testing by executing specific test cases or scenarios that involve data interactions and dependencies. Validate that the data behaves consistently in the modernized application, producing the same results as in the legacy system.

Data Validation and Business Rules

Verify that any business rules or validation checks applied to the data during the migration process function correctly. Test scenarios that involve data validation, calculations, or data-driven business logic to ensure the desired outcomes are achieved.

Performance and Scalability Testing

Test the performance and scalability of data operations in the modernized application. Validate that the data retrieval, insertion, and manipulation processes meet the performance requirements and can handle the expected data volumes and concurrent users.

Backup and Recovery Testing

Test the backup and recovery mechanisms for the migrated data. Validate that the data can be backed up, restored, and recovered successfully in case of data loss or system failures.

Document and Resolve Discrepancies

Document any discrepancies or data inconsistencies found during data migration parity testing. Prioritize them based on their impact and severity, and work closely with the development and data migration teams to address and resolve these discrepancies.

Retest and Regression Testing

After resolving the identified data discrepancies, retest the affected data elements and validate that the changes have been implemented correctly. Conduct regression testing to ensure the fixes do not introduce new data issues or impact existing functionality.

Tools to be considered

Data Comparison Tools: Beyond Compare, WinMerge, Redgate SQL Data Compare.

Data Validation Tools: Informatica Data Validation, QuerySurge, Talend Data Quality.

ETL Testing Tools: Informatica PowerCenter, Talend Open Studio, IBM InfoSphere DataStage.

By following these steps, we can ensure that the data from the legacy system is accurately migrated to the cloud environment, maintaining data integrity and consistency. This helps preserve business-critical information and enables seamless data operations in the modernized application.

Performance parity testing focuses on validating that the modernized application meets or exceeds the performance levels of the legacy system. It ensures the application can handle the expected load, response times, and resource utilization in the cloud environment.

To perform performance parity testing, follow these steps:

Define Performance Benchmarks

Establish performance benchmarks based on the performance metrics of the legacy system. Identify key performance indicators (KPIs) such as response time, throughput, resource utilization, and concurrency levels.

Performance Test Design

Design performance test scenarios that closely replicate the workload patterns observed in the legacy system. Consider user volumes, transaction volumes, peak usage periods, and system interactions.

Test Environment Setup

Set up the performance test environment that resembles the production environment as closely as possible. This includes configuring the cloud infrastructure, networking components, and necessary resources to support the expected workload.

Performance Test Execution

Execute the performance test scenarios on the modernized application to measure its performance. Monitor and collect performance metrics such as response times, CPU utilization, memory usage, network latency, and throughput.

Load Testing

Conduct load testing by gradually increasing the user load on the modernized application to determine its performance limits. Measure and analyze the application's response times, throughput, and resource utilization under different load levels.

Stress Testing

Perform stress testing to evaluate the application's stability and robustness under extreme or peak load conditions. Apply a significantly higher load than the expected capacity to identify performance bottlenecks, resource limitations, or system failures.

Performance Analysis and Comparison

Analyze the performance metrics collected during testing and compare them with the benchmarks established from the legacy system. Identify any significant performance disparities or deviations and investigate the root causes.

Performance Optimization

Collaborate with the development and operations teams to optimize the modernized application's performance based on the findings from performance testing. Implement performance-tuning techniques such as code optimization, caching mechanisms, database query optimization, and infrastructure scaling.

Retesting and Iterative Optimization

After implementing performance optimizations, retest the modernized application to validate the improvements and ensure they do not introduce new issues. Continuously iterate the performance testing and optimization process until the modernized application meets or surpasses the performance levels of the legacy system.

Document and Resolve Discrepancies

Document any performance disparities or regressions found during performance parity testing and prioritize them based on their impact and severity. Work closely with the development team to address and resolve these issues, ensuring the modernized application achieves performance parity.

Tools to be considered

Performance Testing Tools: Blazemeter, Apache JMeter, LoadRunner, Gatling.

APM (Application Performance Monitoring) Tools: New Relic, AppDynamics, Dynatrace.

Load Balancing and Scaling Tools: AWS Elastic Load Balancer, NGINX, HAProxy.

By following these steps, we can validate the performance of the modernized application and ensure that it meets or exceeds the performance levels of the legacy system. This provides a smooth transition to the cloud environment without compromising performance requirements.

Integration parity testing focuses on verifying the interoperability and data exchange between the modernized application and other systems or services. It ensures that the integration points in the modernized application work seamlessly, similar to the legacy system.

To perform integration parity testing, follow these steps:

Identify Integration Points

Identify the integration points in the legacy system, such as APIs, web services, databases, or third-party system integrations. Understand these integrations' data flows, dependencies, and communication protocols.

Replicate Integration Setup

Set up the necessary integration components in the modernized application, such as APIs, web services, or database connections. Configure the integration components to replicate the behaviour and functionality of the legacy system.

Test Data Exchange

Perform data exchange testing by sending and receiving data between the modernized application and the integrated systems. Verify that the data is transmitted accurately and consistently and that the responses from the integrated systems match the expected results.

Validate Message Formats

Ensure that the message formats, data structures, and protocols used for integration remain consistent between the legacy system and the modernized application. Validate that the data sent and received follows the same format and structure as in the legacy system.

Test Error Handling

Test error handling mechanisms in the modernized application during integration scenarios. Simulate error conditions, such as network failures, invalid data, or system unavailability, and verify that the modernized application handles these errors appropriately, similar to the legacy system.

Verify System Dependencies

Identify and validate any dependencies between the modernized application and external systems. Ensure that the dependencies are correctly resolved, and the modernized application can access and utilize the required resources and services as expected.

Performance and Load Testing

Perform performance and load testing on integration points to ensure that the modernized application can handle the expected data volumes and concurrent requests. Validate that the performance of the integration points meets or exceeds the performance levels of the legacy system.

Document and Resolve Discrepancies

Document any discrepancies or deviations found during integration parity testing and prioritize them based on their impact and severity. Collaborate with the development and integration teams to address and resolve these discrepancies, ensuring the modernized application achieves integration parity.

Retest and Regression Testing

After fixing the identified discrepancies, retest the integration points to validate that the changes have been implemented correctly. Conduct regression testing to ensure the fixes do not introduce new issues or impact existing integrations.