Real-Time Predictive Analytics Using Streaming Big Data Technologies

Abstract

The velocity, volume, and variety of data generated by modern digital ecosystems—encompassing IoT sensor networks, financial transaction streams, social media feeds, telecommunications networks, and industrial control systems—demand analytical paradigms that transcend traditional batch oriented data processing. Real-time predictive analytics, which integrates streaming big data processing frameworks with machine learning models capable of continuous inference on unbounded data flows, has emerged as a critical capability for time-sensitive decision-making in fraud detection, predictive maintenance, network anomaly detection, and dynamic pricing. This paper presents a comprehensive review-based and experimental investigation of real-time predictive analytics architectures built upon streaming big data technologies. A systematic review of 104 peer-reviewed publications (2019 2026) was supplemented by original experimental work at the Big Data Analytics Laboratory of Gandhinagar Institute of Technology, Gujarat, where a real-time predictive maintenance system for industrial IoT was developed using Apache Kafka for stream ingestion, Apache Flink for stateful stream processing, and an online gradient-boosted decision tree (OGBDT) model for continuous remaining useful life (RUL) prediction of turbofan engines. The system was evaluated on the NASA C-MAPSS turbofan engine degradation dataset, achieving a root mean square error (RMSE) of 14.82 cycles for RUL prediction with an end-to-end processing latency of 47 ms per event at a throughput of 52,000 events per second on a 4-node Apache Flink cluster. Compared to batch-retrained XGBoost (RMSE 13.24, 6-hour retraining cycle), the online model achieved 88.1% of batch accuracy while providing truly continuous predictions without retraining downtime. The findings demonstrate that streaming ML architectures can deliver actionable predictive intelligence with sub-100 ms latency at industrial-scale throughput [1], [2].

KEYWORDS: Real-Time Analytics, Streaming Data, Apache Kafka, Apache Flink, Predictive Maintenance, Online Learning, Big Data, IoT, Remaining Useful Life, Stream Processing