Get started with AWS Glue Data Quality dynamic rules for ETL pipelines
Big Data Blog
This blog post explains how to use AWS Glue Data Quality dynamic rules to monitor and validate data quality in ETL pipelines. It demonstrates how dynamic rules can automatically adjust thresholds based on historical data trends, eliminating the need to manually update static rules.
Specifically, the article covers:
- Overview of AWS Glue Data Quality dynamic rules
- Setting up resources with AWS CloudFormation
- Implementing a solution with an AWS Glue job using dynamic rules
- Descriptions of various dynamic rule types (CustomSQL, Mean, Sum, RowCount, Completeness, DistinctValuesCount, ColumnCount)
- Running the job incrementally to evaluate dynamic rules
- Analyzing data quality results and failed rules
- Clean up steps
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