A Survey on Modern Cloud-Based Data Engineering Architectures

Modern data engineering has shifted rapidly as organizations increasingly rely on cloud platforms to support large-scale, diverse, and real-time data workloads. Traditional data warehouses—designed primarily for structured data and batch processing—struggle to accommodate modern analytics requirements, leading to the adoption of more flexible and scalable cloud-native architectures. This survey examines the evolution of data engineering architectures in cloud environments, focusing on design principles, core components, architectural patterns, and the challenges associated with operating modern data platforms. The study synthesizes existing literature and industry practices to provide a consolidated reference for practitioners, students, and researchers seeking clarity on the structure, benefits, and trade-offs of contemporary cloud-based data engineering systems.