Building a scalable data ingestion framework for Microsoft Fabric

This article delves into a frequently discussed topic during customer engagements— scaling data engineering for streamlined ingestion and validation processes. How can we enhance the efficiency, reliability, and agility of data engineering? This question is complex, intertwining with various aspects such as desired target architecture, data quality and modeling requirements, metadata management, and more.

One of the main challenges of scaling data engineering lies in the absence of out-of-the-box solutions to handle the repetitive, often tedious tasks of writing code, setting parameters, testing pipelines, and monitoring performance. This issue is predominantly due to the significant variation in requirements across different organizations. Some have highly diversified technology standards for their source systems, while others maintain a simpler landscape with less vendors and complex source systems. Some organizations prefer layering their data extensively, while others are content with the recommended three-layered Medallion Architecture. Some prefer central control over democratizing data curation across different teams.

Irrespective of the complexity and diversity, the goal of most enterprises remains the same — to build a scalable ingestion framework for streamlining data engineering. Let’s discover how this works by building a minimum viable product for Microsoft Fabric.