Building scalable AWS Lake Formation governed data lakes with dbt and Amazon Managed Workflows for Apache Airflow

This article demonstrates building a scalable, governed data lake using AWS Lake Formation, dbt, and Amazon MWAA with automated data quality checks and access control.

• Metadata-driven MWAA framework dynamically generates DAGs for improved pipeline scalability
• dbt with Athena adapter enables modular SQL-based transformations on data lakes
• Automated framework identifies and segregates problematic records maintaining data integrity
• AWS Lake Formation implements fine-grained access controls for Athena tables
• CloudFormation template enables repeatable deployment across AWS services
• Lambda functions automate Lake Formation permissions and tag-based access control
• dbt models include data quality checks with exception handling and audit logging
• Iceberg tables created by dbt automatically receive Lake Formation tags post-run
• Metadata-driven approach scales governance across multiple databases and tables

The solution provides enterprise-grade data governance by combining orchestration, transformation, and access control into a unified, automated framework suitable for large-scale deployments.