Simplifying ETL processes while maintaining flexibility and scalability is a constant challenge in modern data engineering. In this session, we’ll explore how to build a metadata-driven ETL process in Azure Data Factory (ADF) that strikes the perfect balance.

This approach allows you to handle both full and incremental data loads seamlessly, with all configuration managed through an easily editable metadata table. By abstracting the complexities of pipeline logic into metadata, you can:

Add or modify data sources without rewriting pipelines.
Configure full or incremental loads with minimal effort.
Maintain consistency and scalability across a dynamic ETL environment.
I’ll walk through the architecture and practical implementation of this solution, sharing tips, tricks, and lessons learned along the way. Whether you’re new to ADF or looking for advanced strategies to optimize your ETL processes, this session will provide you with the tools and insights to build efficient, metadata-driven pipelines that adapt to your evolving data needs.