Enterprise-Grade Metadata-Driven ETL Framework using Azure Data Factory

Success Metrics

60 %

Reduction in development and maintenance effort

45 %

Improvement in data ingestion reliability

70 %

Reduction in manual deployment issues

Introduction

This project focuses on building an enterprise-grade data integration framework using Azure Data Factory. The solution ingests data from REST APIs and flat files, supports bulk and incremental processing, applies dynamic transformations, and loads curated data into Azure SQL. The primary objective was to design a scalable, reusable, and automated ETL solution aligned with real-world data engineering practices.

Key Highlights

Azure Data Factory pipeline development

CI/CD enablement using GitHub

REST API integration and automation

Metadata-driven ETL framework implementation

Error handling, monitoring, and alerting

Business Challenges

Managing bulk, incremental, and historical backdated data refreshes
Handling REST API pagination for large data volumes
Routing multiple file types dynamically to correct folders
Frequent schema changes causing pipeline failures
Lack of centralized error handling and alerting
No version control for pipeline code

Solution

A metadata-driven Azure Data Factory solution was implemented to handle bulk, incremental, and backdated loads using control tables. REST API ingestion supported pagination rules for complete data retrieval. Dynamic pipelines leveraged ForEach and Switch activities for file routing. Tabular JSON enabled dynamic schema mapping, while Mapping Data Flows handled transformations and upserts. GitHub integration ensured proper version control and collaboration.

Business Impact

The solution reduced manual effort and improved pipeline reliability through automation and dynamic configurations. Reusable pipelines accelerated onboarding of new data sources, while centralized error handling improved monitoring. Schema flexibility minimized maintenance efforts, and version control ensured safer deployments. Overall, the framework delivered a scalable, maintainable, and enterprise-ready data integration solution.