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Answer: Integrating Databricks notebooks with Azure DevOps pipelines, using PyTest for regression testing with mock data sets
The most suitable approach for setting up an automated regression testing framework for data pipelines in Azure Databricks is integrating Databricks notebooks with Azure DevOps pipelines and using PyTest for regression testing with mock data sets. This method leverages Azure DevOps for automation and PyTest for scalable test cases, ensuring robust and error-free data transformation logic. Other options either lack automation (manual test scripts), are less suited for Azure Databricks (GitHub Actions), or are less flexible (Databricks Jobs with Azure Blob Storage).
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To ensure updates to data transformation logic in your Azure Databricks notebooks do not introduce errors or regressions, which automated testing framework should you implement?
A
Setting up a continuous integration/continuous deployment (CI/CD) pipeline using GitHub Actions to run tests against a mirrored production environment
B
Integrating Databricks notebooks with Azure DevOps pipelines, using PyTest for regression testing with mock data sets
C
Utilizing Databricks Jobs with scheduled test runs, comparing outputs to expected results stored in Azure Blob Storage
D
Writing custom test scripts within Databricks notebooks that are manually executed before each deployment
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