Designing Scalable and Reusable ETL Frameworks in Azure Data Factory

As institutions integrate an increasing number of data sources, ETL pipelines in Azure Data Factory (ADF) often become complex, repetitive, and difficult to maintain. Many teams face challenges such as managing numerous datasets, creating separate pipelines for each process, relying on hard-coded configurations, and struggling to scale efficiently.

This session presents a practical approach to designing scalable and reusable ETL frameworks in ADF. It explores how to simplify pipeline architecture by reducing duplication, leveraging parameterization, and using dynamic datasets to move away from rigid designs. The session also highlights how a metadata-driven approach can support flexible and scalable solutions as data environments evolve.

Through real-world patterns and examples, attendees will see how these techniques improve maintainability and efficiency while supporting long-term growth in modern data platforms.