Large Language Models for Enterprise Data Engineering: Automating ETL, Query Optimization & Compliance Reporting (Published)
This article explores the transformative role of Large Language Models (LLMs) in enterprise data engineering, focusing on their capacity to automate ETL processes, optimize queries, and streamline compliance reporting. The article examines how LLMs possess sophisticated capabilities for understanding data structures, generating code, transferring knowledge across platforms, and applying probabilistic reasoning for data quality. It delves into technical implementations of LLM-powered ETL automation, including script generation, schema evolution handling, and integration with modern data stacks. The article further investigates how these models optimize SQL queries and create natural language interfaces, making data more accessible to non-technical users. Through industry case studies in financial services, healthcare, retail, and manufacturing, the article demonstrates how LLMs are delivering substantial improvements in operational efficiency, data utilization, and business outcomes, representing a fundamental shift in how organizations perceive data engineering challenges. It also acknowledges the limitations of current LLM applications in data engineering and suggests directions for future research, including addressing ethical considerations such as potential biases and the need for explainable AI.
Keywords: ETL optimization, data engineering automation, enterprise data governance, natural language interfaces, schema evolution handling
AIDEN: Artificial Intelligence-Driven ETL Networks for Scalable Cloud Analytics (Published)
This article introduces a novel framework for AI-driven cloud data engineering that addresses the growing challenges of scalable analytics in enterprise environments. The article presents an intelligent system architecture that leverages machine learning techniques to dynamically optimize extract, transform, and load (ETL) processes across distributed cloud infrastructures. The approach employs adaptive resource allocation, predictive scaling mechanisms, and metadata-driven processing to significantly enhance data pipeline efficiency while minimizing operational costs. The framework incorporates a self-tuning transformation engine that autonomously manages schema evolution and workload distribution based on historical performance patterns and real-time system metrics. Experimental evaluation across multiple industry scenarios demonstrates substantial improvements in processing throughput, resource utilization, and overall system reliability compared to traditional ETL methodologies. The proposed solution provides data engineers with an adaptive platform that evolves alongside changing data volumes and complexity, offering a promising direction for next-generation enterprise data architectures.
Keywords: Artificial Intelligence, Cloud Computing, ETL optimization, data pipeline automation, scalable analytics