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Ensuring cross-version compatibility for Databricks Notebooks is crucial with the release of new Databricks Runtime versions. What strategy best guarantees that existing notebooks remain compatible and performant across different runtime versions?
A
Manually updating a test environment to new runtime versions as they are released, running a set of benchmark notebooks, and documenting any issues
B
Implementing continuous integration workflows that automatically test notebook compatibility with new runtime versions using Azure DevOps pipelines
C
Setting up parallel environments in Azure Databricks, each running a different runtime version, and executing all notebooks to compare outputs and performance
D
Utilizing Databricks Jobs API to programmatically run notebooks against multiple runtime versions, analyzing logs for errors or performance degradation
