What are the best practices for testing data migration and versioning in a data warehouse schema?

Define a the objective in charter , align everyone to this with to meet business goals. Plan with the priorities , find out high risk areas , track them with the mitigation plan. Try to migrate and use representative data from all perspective of complexities - if using real data consider masking it. Define validations rules - with automation . Measure test coverage based on the priorities and usage . Consider to perform the volume & performance testing if it needs to meet specific NFR. Prepare the test scripts early - if these are repetitive think for automation .Keep provision for documentation and audit of test results .Publish the reports on target vs achieved on regular basis to the key stake holders with the analysis for missing target

Best practices: -Backup Data. -Test Env Setup. -Data Profiling:Conduct through data profiling to understand the characteristics & patterns of your data. -Version Control. -Incremental Testing. -Data Validation:Implement comprehensive data validation checks -Dependency Management:Clearly document & manage dependencies between data objects. -Performance Testing:Consider factors such as query performance & system resource utilization. -Documentation:Is crucial for troubleshooting & future reference. -Security Considerations:Includes access controls, encryption, & compliance with data protection regulations. -Monitoring & Logging:Implement robust monitoring & logging mechanisms to track the progress.

Including your customer, or the subject matter experts, chosen to represent them, is critical at this point. You need to know which KPI’s which other data that the users want to make sure are testing; never assume you know the data to be tested. A business analyst can help you interface with your customer if one is available.

Define clear test objectives upfront aligned to business goals. Focus testing on high-risk areas and integration points. Leverage test data subsets that mirror production data volume and complexity. Automate validation of critical attributes and relationships across systems. Measure test coverage for completeness. Capture performance baselines to monitor system responsiveness over time. Leverage regression testing to validate new features or upgrades. Document detailed test plans with pass/fail criteria for repeatability. Collaborate across teams to confirm synchronised cutover. Continuously improve test automation and framework reusability.

Best Practices: - Employ rigorous testing practices across different phases, including unit, integration, and system testing. - Implement data sampling and validation techniques to verify data consistency. - Utilise data profiling to identify potential quality issues and anomalies. - Test data transformation rules to ensure accurate conversions. - Assess performance and scalability to handle expected data volumes and processing demands. - Automate testing processes to streamline operations and reduce manual effort. - Establish continuous monitoring and alerting mechanisms to detect and address data quality or performance issues promptly. - Maintain comprehensive documentation and KTs to ensure consistent understanding and practices.

Data-Driven Testing: Functions by separating test scripts from data. Allows for execution of the same scripts with various data sets. Typically uses automation frameworks/tools, where test data is stored separately, i.e., external files/databases. This eases the additions of new test cases and reduces need for script modifications. Property-Based Testing: Functions by generating arbitrary data that meets criterion and verifying guarantee remains valid under said conditions. If a data instance is found in violation of guarantee, it systematically reduces the instance to simpler forms recorded for future reference. Writing tests entails defining guarantees/properties that code upholds, regardless of varying inputs/conditions encountered.

Automated Testing: Prioritize automated testing for efficiency and repeatability. Integration Tests: Conduct unit tests for individual components and integration tests for system-level validation. Regression Testing: Employ regression testing tools to ensure existing functionality remains intact. Performance Testing: Use tools to assess data loading, indexing, and query performance under various conditions. Version Contro: Implement robust version control (e.g., Git) for tracking schema changes and collaboration. Continuous Integration/Continuous Deployment: Integrate testing into CI/CD pipelines for streamlined and automated processes. Data Profiling Tools: Utilize data profiling tools to analyze and address data quality issues.

When we reached a few TB, we decided to transition our (outgrown) AWS RDS to Snowflake. The team was small, so we knew we had to use off-the-shelf tools. We utilized Atlan to understand structural differences deeply and ensure Snowflake compatibility for data profiling. After playing with Glue, we chose Fivetran for ELT while mapping our business rules. We created tables to save the checksum of every transformation mapped to our original ELT running on K8S. Thus, our sanity test was asserting checksums. Planning a methodical and tool-specific approach was instrumental in successfully migrating to a more scalable Snowflake environment.

Orchestration Tools: Utilize orchestration tools to sequence and manage automated test execution, ensuring a streamlined workflow. Parameterization: Enable parameterization for flexible test data and scenarios, accommodating diverse conditions. Data Seeding/Cleanup Automation: Automate data seeding and cleanup processes for a consistent and controlled test environment. Versioned Test Data: Align test data with schema versions to simulate real-world scenarios accurately. Error Handling Automation: Develop automated error-handling mechanisms to identify, log, and report issues during testing. Parallel Execution: Optimize testing time and resources by implementing parallel test execution, especially for large datasets.

I totally agree with using automation and orchestration. I would also employ code generation and a standardised framework so that should the automation assets need to change then the manual effort is minimal, rather than having to modify the end code product, and it also ensures that the modified assets are correct from the foundation upwards rather than it being an imperfect fudged code item. The standardisation allowed for lots of other bells and whistles such as statistics reports and for drilling down through reconciliation and validation reports.

Version control is crucial for tracking changes, collaborating with others, and maintaining code integrity and here are some best practices: Branching: Create branches with descriptive branch name for your data warehouse schema change. Commit: Write meaningful commit messages for your schema change. Code Review: Use pull requests for code review. This allows team members to review changes before merging. Keep Commit History Clean: Before pushing changes, squash or reword commits to maintain a clean and logical commit history. Branch Permissions: Use branch restriction to avoid accidental code commit or merge. Backup: Always backup your stable branch before merging any new changes to it.

Branching Strategy: Adopt a branching strategy (e.g., feature branches) for organized and controlled development, testing, and deployment. Descriptive Commit Messages: Provide clear and descriptive commit messages to communicate the purpose and impact of each change. Atomic Commits: Keep commits atomic and focused on specific changes to simplify tracking and rollback if needed. Tagging Releases: Tag releases in the VCS to mark stable points, facilitating easy rollback and version identification. Collaboration Guidelines: Establish clear guidelines for collaboration, conflict resolution, and code review processes within the VCS. CI: Integrate VCS with CI/CD pipelines to automate testing and deployment processes, ensuring consistency.

7 years ago, there wasn't a lot of schema code and configurations versioning in open source... and I learned the worst way. In the early days of Redshift in 2016, a schema update went live without proper review. Reports went off. Dashboards were showing gibberish and worst of all, and we had no easy way to roll back the changes because we did full daily refreshes on a team with 9 working. Oh yeah, it was almost the end of the quarter... We spent 3 days in a frenzy to find the culprit among a haystack of untracked updates. This incident was a wake-up call about the importance of git. We dove into semantic versioning, giving a clear roadmap of our schema's evolution. It wasn't just about avoiding chaos but staying sane.

Regression Testing: - Create a test plan that covers all aspects including data ETL processes, data models, queries, and analytics - Automate testing wherever possible - Create test datasets that represent the current state of the data in the existing data warehouse - Perform baseline, incremental and performance testing to ensure smooth execution of data warehouse post changes Impact Analysis: - Identify patterns, anomalies, and data quality in source and target systems - Analyze mapping and transformation between source and target systems - Identify dependencies between different components of the data warehouseand mutual impact - Validate the results of queries and reports - Assess the security, data privacy and data access, performance

A lot has changed in the data world streamlining how we can tackle challenges including versioning and testing. Whether or not the decision is to follow either a Lakehouse and Warehouse route, technologies like Databricks Delta and Apache Iceberg tackle versioning in the former, while the likes of Snowflake and their Time Travel addresses the latter. Other contributors have touched upon Github, and this is a valuable path to follow for any data professional looking to version their code. There is a lot that we can learn from the software dev community. For years they have tried many testing strategies that we can use in data. Iteration is the key. Testing is throughout not just the end. TDD for data might just be the way forward.

#1-3, and #5 are important. With modern data engineering #4 (although critical) is easily implemented with existing tools! However, it is critical to find out why the migration is happening: 1. Is it because the tool (Oracle vs MySQL vs SQLServer vs BigData and DataBrick/Snowflake) Changed? 2. Do you have fundamental shift in tool (migrating from RDS to MPP or vice versa)? Other Tech notes: 1. Do you have strategy (and verify tools) to ensure whether data would create value in right time for a right stakeholder? 2. Is it purely technical migration or business migration (change in business process and tool)? 2. Do you have capability to create simulated data? 3. Do you have data testing framework?