From raw to refined: building a data quality pipeline with AWS Glue and Amazon S3 Tables

This article discusses building a data quality pipeline using AWS Glue and Amazon S3 Tables to improve data lake management and analytics workloads.

• AWS Glue Data Quality helps measure, monitor, and improve data quality using machine learning
• Amazon S3 Tables provides scalable storage with Apache Iceberg support for tabular data
• The solution demonstrates a workflow to validate and separate data into curated and rejected tables
• Key features include:
  • Automatic data validation using predefined and custom rules
  • Separation of high-quality and low-quality records
  • Streamlined data operations using S3 Tables
• The process helps organizations improve data reliability for AI and analytics workloads

The solution provides an efficient method to ensure data quality, reduce manual effort, and generate more accurate insights from data lakes.