Build AWS Glue Data Quality pipeline using Terraform

This article demonstrates how to build AWS Glue Data Quality pipelines using Terraform, implementing two complementary validation approaches for comprehensive data quality monitoring.

• ETL-based Data Quality validates data during transformation, generating detailed metrics and row-level outputs
• Catalog-based Data Quality validates data at rest against Glue Data Catalog tables independently
• Solution uses NYC yellow taxi dataset with 8 validation rules covering completeness, accuracy, and consistency
• Terraform deploys S3 buckets, IAM roles, Glue jobs, crawlers, and CloudWatch monitoring automatically
• ETL approach ideal for catching issues early in data pipelines; catalog approach for ongoing data lake monitoring
• Both methods generate detailed quality scores and store results in S3 for analysis
• Infrastructure-as-code approach enables version control, reproducibility, and multi-environment deployment
• GitHub repository includes complete Terraform configuration and Python scripts for immediate deployment

This solution provides organizations with automated, scalable data quality validation using serverless AWS Glue and infrastructure-as-code best practices.