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Answer: Develop PyTest test cases that can be run as part of a CI/CD pipeline in Azure DevOps, validating data outputs against expected results.
Developing PyTest test cases that can be integrated into a CI/CD pipeline in Azure DevOps is the most efficient strategy for automating tests to ensure data integrity and accuracy before deploying updates to data transformation jobs in Databricks. PyTest is a widely used testing framework in the Python community that allows for easy and efficient test case development. Integrating these PyTest test cases into a CI/CD pipeline in Azure DevOps allows for automated testing to be seamlessly incorporated into the deployment process, ensuring data integrity and accuracy are consistently checked before updates are deployed to production. This approach saves time and effort compared to manual methods, providing a more efficient and reliable way to ensure data integrity and accuracy in your data transformation jobs.
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To ensure data integrity and accuracy before deploying updates to your data transformation jobs in Databricks, which automated testing framework strategy would you implement?
A
Utilize Databricks Jobs API to schedule and run data validation scripts automatically before updates are deployed to production.
B
Develop PyTest test cases that can be run as part of a CI/CD pipeline in Azure DevOps, validating data outputs against expected results.
C
Leverage Databricks MLflow to track data metrics over time, manually reviewing these before approving deployments.
D
Manually execute a set of SQL queries in Databricks notebooks pre- and post-deployment to check data consistency.
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