AI-Driven ETL pipelines for real-time business intelligence: A framework for next-generation data processing

This article explores the transformative potential of AI-driven ETL (Extract, Transform, Load) pipelines for real-time business intelligence. Traditional ETL processes face significant challenges in today's data-intensive environment, including scalability limitations, processing latency, and maintenance complexities. The article examines how artificial intelligence and machine learning can revolutionize data processing through predictive transformation patterns, automated schema evolution, and intelligent resource allocation. By implementing modular, event-driven architectures with advanced anomaly detection and dynamic workload balancing, organizations can achieve substantial improvements in processing efficiency, data quality, and analytical timeliness. The article presents a comprehensive framework for AI-driven ETL implementation, covering architectural components, integration strategies, and performance evaluation metrics across diverse industry applications. This article enables organizations to transition from batch-oriented to real-time analytics while significantly reducing operational costs and expanding business intelligence capabilities.