Automated ETL Pipelines for Modern Data Warehousing: Architectures, Challenges, and Emerging Solutions

Abstract

The paper addresses the evolution of automated Extract, Transform, Load (ETL) pipelines in contemporary data warehousing environments, highlighting their essential role in enabling timely analytics and business intelligence. Recent architectural approaches like cloud-native ETL, stream processing architectures, and metadata-driven automation are addressed in the context of increasing data volume and variety. The article addresses typical challenges like schema evolution management, data quality assurance, and cross-platform integration in the context of discussing novel solutions based on leveraging artificial intelligence for pipeline optimization. Through a survey of current implementations and future perspectives, this research provides an in-depth view of how automated ETL workflows are transforming data warehouse environments and enabling more agile, scalable business intelligence solutions.