
A reusable metadata-driven ETL framework using AWS services like Glue, Airflow, and EventBridge simplifies lake house data pipeline development by reducing boilerplate code, improving error handling, and enabling faster deployment of scalable data ingestion patterns.


<div><p>This article presents a reusable ETL framework for AWS lake house architectures that addresses common data pipeline challenges.</p><ul><li>Lake house has five layers: landing, raw, stage, presentation, and data warehouse</li><li>Metadata-driven framework uses pre-created AWS Glue templates for common ETL tasks</li><li>Supports both push-based and pull-based data ingestion patterns</li><li>Amazon MWAA orchestrates pipelines; EventBridge schedules DAGs; RDS stores metadata</li><li>Framework reduces boilerplate code and improves pipeline development speed</li><li>Dynamic AWS Glue job creation and deletion based on configuration</li><li>Centralized error handling with SNS notifications for failures</li><li>Eliminates need for hundreds of individual Glue jobs per organization</li></ul><p>The framework standardizes data pipeline development, improves maintainability, and enables faster time-to-market for new data pipelines in large-scale lake house environments.</p></div>


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