Optimizing Data Pipelines for Real-Time Healthcare Analytics in Distributed Systems: Architectural Strategies, Performance Trade-offs, and Emerging Paradigms

The growing complexity and volume of healthcare data necessitate highly optimized real-time analytics systems capable of supporting clinical decision-making and operational efficiency. This study investigates architectural strategies for optimizing data pipelines in distributed healthcare analytics environments. It evaluates key performance metrics such as latency, throughput, scalability, reliability, and data consistency across multiple pipeline architectures, including Lambda, Kappa, and Micro-Batch (Spark). Using synthetic healthcare datasets and performance benchmarks, we highlight trade-offs between latency and operational costs, emphasizing the critical balance between system efficiency and clinical utility. Emerging paradigms such as edge computing, AI-driven optimization, and adaptive resource management are explored as pathways to enhance resilience and performance. The findings provide actionable insights for designing adaptive, secure, and cost-effective healthcare data pipelines capable of meeting stringent real-time demands.