AI-Driven Cloud Solutions for Robust Data Engineering: Addressing Challenges and Opportunities

Abstract

Cloud computing has developed at a very fast pace and has transformed data engineering for organizations to handle big data. But limitations including reliability of data, the ability to expand the cloud-based systems, and security become major issues. This paper discusses the use of artificial intelligence (AI) in solving these challenges with techniques to improve the reliability of the cloud data engineering. Through the integration of AI algorithms such as predictive analysis, anomaly detection, and automated optimization, the findings of this research highlight how data reliability increases and how the scalability and security compliance of the system enhance. Altogether, the research compares AI with the existing literature study, experiments it on cloud platforms, and benchmarks it with traditional approaches to demonstrate how AI can improve data work flows, minimize operating expenses, and support better decision making. The results evidence the capabilities of AI in combination with cloud solutions in establishing effective and progressive data engineering structures that can advance further as a field.