Designing a metadata-driven ETL framework with Azure ADF: An architectural perspective

In right this moment’s data-driven panorama, integrating various knowledge sources right into a cohesive system is a fancy problem. As an architect, I got down to design an answer that might seamlessly join on-premises databases, cloud functions and file programs to a centralized knowledge warehouse. Conventional ETL (extract, rework, load) processes usually felt inflexible and inefficient, struggling to maintain tempo with the fast evolution of information ecosystems. My imaginative and prescient was to create an structure that not solely scaled effortlessly but additionally tailored dynamically to new necessities with out fixed guide rework. 

The results of this imaginative and prescient is a metadata-driven ETL framework constructed on Azure Information Manufacturing facility (ADF). By leveraging metadata to outline and drive ETL processes, the system gives unparalleled flexibility and effectivity. On this article, I’ll share the thought course of behind this design, the important thing architectural choices I made and the way I addressed the challenges that arose throughout its improvement. 

Recognizing the necessity for a brand new strategy 

The proliferation of information sources — starting from relational databases like SQL Server and Oracle to SaaS platforms like Salesforce and file-based programs like SFTP — uncovered the constraints of typical ETL methods. Every new supply usually requires a custom-built pipeline, which rapidly turned a upkeep burden. Adjusting these pipelines to accommodate shifting necessities was time-consuming and resource-intensive. I noticed {that a} extra agile and sustainable strategy is crucial.