How do you test and validate data models after updating them?

Before reviewing changes, leverage insights on the importance of understanding the business context behind data model changes. Consider documenting not only technical modifications but also the business rationale for those changes. Additionally, emphasize the significance of version control to track changes efficiently.

Beyond simply documenting changes, integrating a change impact analysis offers significant value. This analysis evaluates the potential effects of data model updates on existing processes, applications, and user experiences. Incorporating a risk assessment framework to identify and mitigate any potential negative impacts before they occur can greatly enhance the update process. By understanding the broader implications of model changes, teams can develop more strategic testing and validation plans that ensure not only technical integrity but also operational continuity.

To test and validate updated data models, start by comparing model outputs with known values to check accuracy. Conduct diagnostic tests to ensure model stability across various scenarios, including edge cases. Use cross-validation, splitting data into training and testing sets, to assess predictive performance and generalizability. Calculate metrics like precision, recall, and RMSE to evaluate accuracy. Finally, review the model for consistency with business logic and domain knowledge, ensuring outputs are realistic and applicable. This approach ensures the model's reliability and effectiveness post-update.

After making changes to the data model, it's crucial to review and document them clearly. This includes identifying the tables, columns, keys, indexes, or views that were added, modified, or deleted. It's important to explain the reasons behind these changes and how they impact the data quality, integrity, and usability. Additionally, comparing the updated data model with the previous version can help identify any discrepancies or conflicts. This will aid in planning the testing and validation process and effectively communicating the changes to stakeholders. Let's make sure we have a thorough review of the changes before moving forward.

The approach taken for this will vary with which technologies are being used for data transformations. At the moment I mainly work in DBT stacks which allows for schema checks and data quality tests to be easily defined as code in your data modeling project. I recommend defining schemas and data quality checks from the outset deriving them from data models that appropriately meet the business requirements.

Get an inventory of what are the changes and focus on how those changes will impact downstream consumption. If there are reports/dashboards being generated, make sure you compare the "before and after" across a period of time to ensure they are not negatively impacted. Also, if changes were made, the odds are you are also anticipating new functionalities: let's have test cases ready to test those new scenarios to make sure the new model supports them. Lastly (or may be it really should be the first step), walk through the data sources and make sure the data relationships found in the sources, and the agreed upon business context, are all in alignment with the changes. This step is crucial to ensure the extensibility of the model!

After reviewing changes to your data model, it is essential to conduct thorough testing and validation, ensuring the functionality and integrity of of your new model. Best practices here involve several steps. First, assess whether each component of the data model performs as expected. Then conduct integration testing to confirm those components are compatible with and mesh well with external systems. Finally, and most critically, it's necessary to utilize regression testing and other analytical methodologies to guarantee none of the existing functionality was compromised by the updates. Combined, these steps will ensure optimal performance of your updated data models with minimal disruption to established functionalities.

Augment the testing methodologies with the use of synthetic data for testing edge cases and corner scenarios. Leverage testing frameworks and tools for automation to ensure comprehensive and repeatable testing. For Unit Testing -Test individual components of the data model in isolation to ensure their functionality independently meets specifications. For Integration Testing - Verify compatibility and coordination between different components and systems, ensuring seamless interaction. For Regression Testing - Confirm that no existing functionality is compromised or diminished after each update, maintaining overall system integrity.

Once the changes to the data model are made, it's important to test its functionality. This involves verifying that the data model can support the intended operations, queries, and transactions without any errors or performance issues. There are various methods and tools available for testing, such as unit testing, integration testing, and regression testing. Unit testing allows us to test each component of the data model individually, while integration testing ensures compatibility and coordination between components and other systems. Regression testing helps ensure that no existing functionality is broken or degraded after each change.

Changes can be deployed in a UAT environment which will follow a set of regression tests that can be compared to the production environment. Lately, I am a fan of only having a single source of datasets so a production and a UAT environment will be built from the same source of data. As I am currently working in DBT stacks, the order of testing I work in goes from dbt compilation checks, query syntax checks, schema checks, basic data quality checks (column level,), more advanced data quality checks (across multiple rows or tables such as referential integrity checks) and then aggregate checks which will run aggregate queries and compare to production data models for comparison.

The final step in the process is to validate the data stored or generated by the data model. This means checking that the data is accurate, complete, and consistent with the business rules and logic. There are various tools and methods that can be used for this, such as data quality checks, data profiling, and data governance. Data quality checks involve identifying errors, anomalies, duplicates, outliers, or missing values in the data and making the necessary corrections or transformations. Data profiling helps analyze the characteristics of the data and compare them to the expected values. Data governance involves establishing and enforcing standards, policies, and procedures to manage the data .

Leveraging advanced data quality tools that incorporate machine learning can offer deeper insights into data integrity and accuracy. These tools can automatically detect anomalies, outliers, or patterns of inconsistency that may not be apparent through traditional validation methods. Additionally, considering data governance principles during validation can ensure that updates comply with data privacy and security regulations, which is crucial in maintaining trust and legal compliance.

Beyond basic validation, consider implementing data validation frameworks that allow for reusable validation scripts, ensuring consistency in how data is checked across different models and updates. Use sampling techniques to validate large datasets efficiently, balancing thoroughness with resource constraints. Additionally, validation should include business logic checks that verify that data transformations align with business expectations, not just technical accuracy. Leveraging data observability tools can provide ongoing validation and alert you to issues as they arise in production environments.

Validation checks can be stored as test artifacts from all the checks used to validate the data model changes. Automation will significantly increase developer velocity so it is important to implement these checks in a CI/CD pipeline and then when deployed in a production environment continuously data quality checks and update metadata for each of the data assets.

- Maintain comprehensive documentation covering technical details and business perspectives. -Promote collaborative review practices to leverage diverse perspectives for identifying potential issues and fostering a culture of knowledge sharing within the team. -Regularly perform backup (automated backup procedures can be implemented) and recovery testing to ensure data integrity and business continuity. -Utilize version control tools like Git to track changes effectively and collaborate efficiently. (alternative Microsoft's Team Foundation Version Control- a centralized version control system integrated with Visual Studio and Azure DevOps Services) This helps in maintaining a clear audit trail of changes and ensuring a reliable history.

Some may include: Performance optimization: Assessing the efficiency and scalability of the updated models to ensure they can handle increasing data volumes and processing demands. Documentation update: Updating documentation to reflect the changes made to the data models, including any new features, functionalities, or dependencies introduced. Stakeholder communication: Keeping stakeholders informed about the updates to the data models, highlighting the benefits, implications, and any associated risks. By addressing these additional considerations, you can further enhance the effectiveness, reliability, and usability of the updated data models.