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Answer: Implement automated checks using AWS Glue DataBrew to identify and flag issues, then use conditional transformations to handle them.
Implementing automated checks using AWS Glue DataBrew ensures that data quality issues are identified early and can be handled systematically, reducing manual intervention and ensuring the integrity of the processed data.
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You are working with a financial dataset that requires high data quality for accurate reporting. Describe how you would run data quality checks while processing the data using AWS Glue DataBrew and what steps you would take to handle any issues identified, such as missing values or incorrect data types.
A
Ignore the issues and continue processing.
B
Stop the processing and manually fix the issues.
C
Implement automated checks using AWS Glue DataBrew to identify and flag issues, then use conditional transformations to handle them.
D
Delete the problematic records and proceed with processing.
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