Adaptive Self-Healing and Cost-Aware Real-Time Data Pipeline Framework with Reinforcement-Based Routing and Anomaly-Aware Optimization

The modern real-time pipelines of streaming data are under the heavy burden of adapting to a dynamically varying workload, anomaly situations, and evolving cloud costs which lead to poor performance and large overheads in operation. The existing systems largely rely on either fixed routing configurations and reactive control, lacking architectures to integrate reactive routing, anomaly detection, and optimisation of costs in a closed loop. In order to fill this gap, the present paper will suggest a new adaptive, self-healing, and cost-aware streaming data pipeline architecture comprising of Adaptive Routing Engine (ARE), Smart Buffering Mechanism (SBM), Self-Healing Pipeline Controller (SHPC), Intelligent Schema Evolution Engine (ISEE), AI-driven Anomaly Detection Layer (A2DL), and Cost Optimization Engine (COE This framework employs decision policies based on reinforcement and feedback control loops to optimize dynamically routing, buffering and recovery actions under different workload and failure conditions. Experimental trials on real and artificial streaming data demonstrate that the suggested system can decrease up to 25-40 percent of delays, 15-30 percent of expenses and enormously mean time to recuperate (MTTR) of that of base static pipelines. The primary contributions of this work include (i) a shared infrastructure of adaptive and resilient streaming systems, (ii) a cost-latency-reliability optimization model that is formalized and (iii) an experimental pipeline that, through measurable results, can compare the next-generation cloud-native streaming infrastructures.