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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 tasked with ensuring data quality for a large dataset stored in S3 that is processed using AWS Glue. Describe a scenario where you would run data quality checks while processing the data and explain how you would handle any issues identified, such as empty fields or inconsistent data formats.
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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