The rapid growth of digital services has compelled enterprises to transition from tightly coupled monolithic infrastructures to distributed platforms operating within cloud-centric environments. Traditional enterprise systems, designed for stable workloads and localized users, are no longer sufficient to meet modern expectations of global accessibility, uninterrupted availability, and continuous feature evolution. Cloud computing introduces elastic resource provisioning and on-demand scalability, while distributed architectural paradigms enable applications to be decomposed into independently deployable services that evolve without disrupting the overall system. Together, these paradigms enable organizations to deliver responsive and resilient services across geographically dispersed user bases. Despite these advantages, the migration to distributed cloud platforms introduces significant engineering complexity. Inter-service communication over unreliable networks requires robust coordination mechanisms, and maintaining data integrity across distributed databases demands carefully designed consistency strategies. Security boundaries expand due to exposed APIs and multi-tenant environments, necessitating identity-centric security models. Furthermore, observability becomes challenging because system behavior must be analyzed across numerous interacting services rather than single hosts, and operational overhead increases as infrastructure becomes highly dynamic and ephemeral. This review analyzes the foundational principles, architectural patterns, enabling technologies, and operational methodologies involved in engineering distributed enterprise platforms. It discusses microservices architecture, containerization and orchestration frameworks, distributed data management approaches, automated DevOps pipelines, observability practices, and zero-trust security models. Engineering trade-offs related to latency, reliability, fault tolerance, and cost efficiency are examined to provide a balanced perspective on system design decisions. The paper also explores emerging directions shaping next-generation enterprise computing, including serverless platforms that abstract infrastructure management, AI-driven operational analytics for predictive reliability, and edge–cloud integration for latency-sensitive workloads. By synthesizing current practices and research challenges, this review aims to provide a comprehensive conceptual framework that assists engineers, architects, and researchers in designing scalable, reliable, and maintainable enterprise systems in modern cloud ecosystems.
Malsha Rodrigo (Sun,) studied this question.