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In right this moment’s data-driven panorama, integrating various information sources right into a cohesive system is a posh problem. As an architect, I got down to design an answer that would seamlessly join on-premises databases, cloud purposes and file programs to a centralized information warehouse. Conventional ETL (extract, rework, load) processes typically 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 in addition tailored dynamically to new necessities with out fixed handbook rework.
The results of this imaginative and prescient is a metadata-driven ETL framework constructed on Azure Data Factory (ADF). By leveraging metadata to outline and drive ETL processes, the system presents 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 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 restrictions of typical ETL methods. Every new supply sometimes 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 spotted {that a} extra agile and sustainable strategy is crucial.
