Cloud-Based Intelligent Data Analytics Architecture for Scalable and Secure Computing Applications
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Abstract
The rapid growth of cloud computing, Internet of Things (IoT), artificial intelligence (AI), and big data technologies has fundamentally transformed the way organizations collect, store, process, and analyse massive volumes of digital information. Modern enterprises, healthcare systems, financial institutions, smart manufacturing environments, and government organizations increasingly rely on cloud infrastructures to support intelligent data analytics and real-time decision-making. However, conventional cloud-based data processing architectures face significant challenges related to scalability, security, privacy preservation, latency, resource optimization, and heterogeneous data integration. These limitations highlight the need for intelligent cloud architectures capable of delivering scalable, secure, and efficient data analytics services.
This study proposes a Cloud-Based Intelligent Data Analytics Architecture (CIDAA) that integrates cloud computing, edge computing, artificial intelligence (AI), machine learning (ML), big data analytics, containerized microservices, and zero-trust security mechanisms for scalable and secure computing applications. The proposed architecture consists of six interconnected layers: Data Acquisition Layer, Edge Intelligence Layer, Cloud Analytics Layer, AI Decision Support Layer, Security and Governance Layer, and User Service Layer. The framework enables distributed processing, intelligent workload scheduling, automated resource provisioning, predictive analytics, and secure multi-tenant data management.
Experimental evaluation was conducted using a heterogeneous dataset containing 8.4 million records collected from healthcare information systems, industrial IoT platforms, financial transaction networks, smart city infrastructures, cybersecurity monitoring systems, and e-commerce platforms between 2021 and 2025. The architecture was implemented using Apache Spark, Hadoop Distributed File System (HDFS), Kubernetes, Docker, TensorFlow, PyTorch, PostgreSQL, Apache Kafka, and Microsoft Azure Cloud. Performance was evaluated using throughput, latency, scalability, resource utilization, prediction accuracy, security resilience, fault tolerance, and cost efficiency.
Experimental results indicate that the proposed architecture improves analytics throughput by 33%, reduces response latency by 29%, increases resource utilization by 31%, enhances security resilience by 28%, and improves overall system scalability by 35% compared with conventional cloud analytics platforms. AI-driven workload optimization and explainable decision-support mechanisms further improved operational efficiency and user confidence.
Despite these improvements, challenges remain regarding interoperability, data governance, energy consumption, regulatory compliance, vendor lock-in, and quantum-resistant cybersecurity. The study concludes that cloud-based intelligent analytics architectures provide a robust foundation for next-generation scalable computing systems. Future research should focus on federated cloud intelligence, confidential computing, digital twins, serverless AI, green cloud computing, and quantum-secure cloud infrastructures.
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References
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