The proliferation of Internet of Things (IoT) devices, sensors, and connected infrastructure in urban environments has led to an explosion of data generation, presenting both opportunities and challenges for smart city development. Cloud computing offers a scalable, flexible, and cost-effective platform to store, process, and analyze this big data. This paper explores comprehensive cloud-based architecture designed specifically for big data analytics in smart cities. The proposed framework integrates data ingestion layers, real-time and batch processing modules, storage repositories, and advanced analytics engines, all hosted in a multi-cloud or hybrid cloud environment. The architecture supports key urban applications such as traffic optimization, energy management, waste disposal, and public safety. Additionally, the paper discusses critical challenges including data security and privacy, latency, interoperability among heterogeneous systems, and governance issues. Emerging technologies such as edge computing, federated learning, and AI-as-a-Service (AIaaS) are also evaluated for their role in augmenting cloud capabilities. By addressing these technical and operational issues, the paper aims to contribute toward the realization of resilient, efficient, and sustainable smart city ecosystems.
Smart Cities, Cloud Computing, Big Data Analytics, IoT, Urban Infrastructure, Edge Computing, Hybrid Cloud, Data Privacy, Real-Time Processing, AIaaS, Federated Learning, Urban Governance