ADAPTIVE EVENT PROCESSING FRAMEWORK FOR REAL-TIME ENTERPRISE DATA PIPELINES

Abstract

The rapid growth of enterprise digital platforms has resulted in continuous generation of highvolume event data from cloud applications, business systems, IoT devices, financial transactions, customer platforms, and distributed enterprise services. Traditional data processing approaches often fail to address the requirements of modern organizations due to increased latency, limited scalability, inefficient resource utilization, and inability to dynamically adapt to changing workloads. This paper proposes an Adaptive Event Processing Framework for Real-Time Enterprise Data Pipelines that integrates event-driven architecture, distributed stream processing, cloud-native computing, Machine Learningbased optimization, and intelligent workload adaptation mechanisms. The proposed framework enables continuous event ingestion, real-time processing, dynamic resource allocation, automated pipeline optimization, anomaly detection, and scalable enterprise integration. Machine Learning models analyze event patterns, processing performance, workload variations, and operational conditions to optimize data pipeline behavior and improve processing efficiency. The framework supports integration with enterprise applications, APIs, databases, IoT platforms, and cloud services while maintaining fault tolerance, scalability, and security. Experimental analysis demonstrates that the proposed adaptive event processing framework improves throughput, reduces processing latency, enhances resource utilization, increases fault tolerance, and provides superior scalability compared with conventional static event processing architectures. The proposed solution establishes an intelligent and flexible foundation for nextgeneration enterprise data pipelines requiring continuous real-time analytics and adaptive processing capabilities. Keywords: Adaptive Event Processing, RealTime Data Pipelines, Enterprise Integration, Stream Processing, Event-Driven Architecture, Cloud Computing, Machine Learning, Big Data Analytics, Distributed Systems, Data Pipeline Optimization.