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Answer: Implement unit and integration tests within Databricks notebooks that validate data outputs against a controlled set of test data, integrating these tests into your CI/CD pipeline.
Implementing unit and integration tests within Databricks notebooks is a proactive approach to ensuring data quality before deploying the updated pipeline to production. By validating data outputs against a controlled set of test data, you can identify any discrepancies or issues in the data transformation process. This allows you to catch potential errors early on and make necessary adjustments to improve data quality. Integrating these tests into your CI/CD pipeline ensures that data quality checks are automated and run consistently every time there is a code change or update to the pipeline. This helps in maintaining data quality standards and prevents regressions from being introduced during the deployment process. Conducting manual data validation, as suggested in option C, can be time-consuming and prone to human error. It may not be efficient or scalable, especially for a significant update to a data transformation pipeline. Using Azure Data Factory to orchestrate a parallel run of both the current and updated pipelines, as mentioned in option A, can be complex and may not provide detailed insights into data quality issues. Leveraging Databricks MLflow, as mentioned in option D, can be beneficial for tracking experiment runs and monitoring data quality metrics. However, using statistical analysis alone may not be sufficient to ensure data quality meets predefined thresholds. Implementing unit and integration tests provides a more comprehensive approach to validating data outputs and identifying potential issues before deployment. Overall, implementing unit and integration tests within Databricks notebooks and integrating them into your CI/CD pipeline is the most suitable strategy for ensuring data quality before production deployment. It allows for automated and consistent data quality checks, helping to maintain or improve data quality standards in the updated pipeline.
Author: LeetQuiz Editorial Team
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You are about to deploy a major update to a data transformation pipeline in Azure Databricks. What is the best strategy to ensure the updated pipeline maintains or enhances data quality before going live?
A
Use Azure Data Factory to orchestrate a parallel run of both the current and updated pipelines, comparing outputs for discrepancies.
B
Implement unit and integration tests within Databricks notebooks that validate data outputs against a controlled set of test data, integrating these tests into your CI/CD pipeline.
C
Conduct manual data validation by comparing outputs from the updated pipeline against expected results for a sample of test data.
D
Leverage Databricks MLflow to track experiment runs with the new pipeline version, using statistical analysis to ensure data quality metrics meet predefined thresholds.
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