Abstract

This paper presents the development of an automated Extract, Transform, Load (ETL) pipeline model aimed at enhancing data quality and governance for improved business intelligence and decision-making. The model integrates advanced data processing technologies, ensuring high-quality data extraction, consistent transformation, and seamless loading into data warehouses. The pipeline model enables organizations to achieve reliable analytics outcomes and optimized decision-making by embedding automated quality checks and governance measures. The study explores key components of the ETL pipeline, discusses the technologies utilized, and provides case studies illustrating the model's application in real-world scenarios. Furthermore, the paper addresses the challenges associated with adopting automated ETL systems, such as data integration complexity and resistance to change, offering recommendations for organizations seeking to leverage this technology for improved business performance. Finally, the study identifies avenues for future research, including the integration of artificial intelligence into ETL processes and the broader application of the model across various industries.