Build Write-Audit-Publish pattern with Apache Iceberg branching and AWS Glue Data Quality

This article explores strategies for managing data quality when ingesting data into Apache Iceberg tables using AWS Glue Data Quality and Iceberg branches, focusing on the Write-Audit-Publish (WAP) pattern.

• Two primary data quality management strategies are discussed:
  • Dead-Letter Queue (DLQ) approach
  • Write-Audit-Publish (WAP) pattern
• The WAP pattern involves three key stages:
  • Write: Data is written to a staging branch
  • Audit: Quality checks are performed
  • Publish: Validated data is merged into the main branch
• Benefits of the WAP pattern with Iceberg branches include:
  • Simplified data management
  • Handling concurrent writers
  • Flexible schema evolution
• The article demonstrates the WAP pattern using a home monitoring system tracking room temperature and humidity data

By using the WAP pattern with Iceberg branches and AWS Glue Data Quality, organizations can ensure only high-quality data is published and used for downstream analysis.