Year: 2025
Type: article
Abstract: Artificial intelligence has fundamentally transformed Extract, Transform, Load (ETL) processes across enterprise environments, revolutionizing traditional data integration practices. Conventional ETL methodologies have historically suffered from labor-intensive manual coding, complex data mapping requirements, and inflexible rule-based architectures, creating bottlenecks in terms of scalability, efficiency, and adaptability. The emergence of AI-enhanced ETL technologies represents a paradigm shift, introducing unprecedented levels of automation and intelligence throughout the data integration lifecycle. Key capabilities include automated schema mapping through semantic analysis and pattern recognition algorithms, intelligent data quality management with real-time anomaly detection, cognitive data classification for sensitive information, and natural language interfaces democratizing access to ETL functionality. Implementation examples across Microsoft Azure environments demonstrate substantial improvements in all ETL phases, while applications in financial services, healthcare, and retail illustrate tangible business value. Looking forward, emerging trends such as autonomous self-configuring pipelines, explainable AI mechanisms, edge-based processing architectures, federated learning frameworks, and quantum-enhanced transformations promise to further revolutionize data integration practices. This technological evolution enables organizations to process increasingly complex data landscapes with enhanced efficiency, accuracy, and agility while reducing operational overhead
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4411519844