Summary

The article discusses the implementation of AWS Glue Data Quality pipelines using Terraform, highlighting two methods: ETL-based and Catalog-based Data Quality validation. It explains how these methods can ensure comprehensive data quality across data lakes and pipelines, utilizing a real-world dataset of NYC yellow taxi trips. The article emphasizes the benefits of Infrastructure as Code (IaC) practices for consistent and repeatable deployments, and provides a step-by-step guide for setting up the necessary resources in AWS.

💭 Your Thoughts

Catalog-based Data Quality – Validates data directly against Glue Data Catalog tables without requiring ETL execution, ideal for monitoring data at rest. This is new for me, but where come from all these metrics in Glue data catalog? We need regularly compute no?

Data Problems Addressed

Technologies Referenced