In at present’s data-driven panorama, integrating various knowledge sources right into a cohesive system is a posh problem. As an architect, I got down to design an answer that might seamlessly join on-premises databases, cloud purposes and file methods to a centralized knowledge warehouse. Conventional ETL (extract, rework, load) processes usually felt inflexible and inefficient, struggling to maintain tempo with the speedy evolution of information ecosystems. My imaginative and prescient was to create an structure that not solely scaled effortlessly but in addition tailored dynamically to new necessities with out fixed guide rework.Â
The results of this imaginative and prescient is a metadata-driven ETL framework constructed on Azure Knowledge Manufacturing facility (ADF). By leveraging metadata to outline and drive ETL processes, the system affords unparalleled flexibility and effectivity. On this article, I’ll share the thought course of behind this design, the important thing architectural selections I made and the way I addressed the challenges that arose throughout its growth.Â
Recognizing the necessity for a brand new strategyÂ
The proliferation of information sources — starting from relational databases like SQL Server and Oracle to SaaS platforms like Salesforce and file-based methods like SFTP — uncovered the constraints of typical ETL methods. Every new supply usually requires a custom-built pipeline, which shortly grew to become a upkeep burden. Adjusting these pipelines to accommodate shifting necessities was time-consuming and resource-intensive. I spotted {that a} extra agile and sustainable strategy is crucial.Â
