AI-Assisted Data Pipeline Orchestration for Scalable Analytics

Abstract

Modern enterprises face increasing demands for scalable and efficient data processing due to rapid data growth. Traditional data pipeline orchestration methods, which rely on static configurations and manual intervention, often lead to inefficiencies in resource use, latency, and fault tolerance. This paper proposes an AI-assisted orchestration framework that integrates machine learning techniques to enable dynamic scheduling, workload prediction, anomaly detection, and resource optimization. By leveraging reinforcement learning, supervised learning, and heuristic methods, the system adapts pipeline configurations in real time based on changing workloads and system conditions. The proposed architecture includes data ingestion modules, AI-driven orchestration engines, adaptive schedulers, and monitoring systems. A key contribution is an intelligent scheduling mechanism that improves execution efficiency and resource utilization. Experimental results show significant improvements over traditional systems, with up to 35% increase in processing efficiency and 25% reduction in latency. The study concludes that AI-driven orchestration is a promising approach for building scalable and autonomous data processing systems, with future work focusing on deeper integration of advanced learning models and real-time adaptability.