Scalable Data Transformations and Real-Time Processing in Healthcare Cloud Platforms

Abstract

Healthcare organizations face unprecedented challenges in managing rapidly growing volumes of heterogeneous data from electronic health records, medical imaging, wearable devices, and IoT sensors. Traditional data processing systems with batch-oriented workflows and on-premises infrastructure increasingly fail to meet modern healthcare demands. Cloud-native platforms have emerged as compelling solutions, offering the scalability, flexibility, and performance required for real-time healthcare data processing. These platforms leverage distributed computing, containerization, and microservices architectures to handle healthcare data at scale while maintaining essential security and compliance standards. Event-driven processing models, serverless computing, and distributed frameworks enable healthcare organizations to implement sophisticated real-time analytics, clinical decision support, and predictive capabilities previously impossible with traditional architectures. Integrating AI, machine learning, and edge computing further enhances these platforms, creating hybrid architectures that optimize performance and resource utilization across the healthcare ecosystem.