Optimizing Data Pipeline Efficiency with Machine Learning Techniques

Abstract

In the era of big data, efficient data processing is crucial for timely insights and decision-making. Traditional data pipelines face challenges such as latency, scalability, and fault tolerance. This paper explores the application of machine learning (ML) techniques to optimize data pipeline efficiency. We propose a framework that integrates ML models for predictive resource allocation, anomaly detection, and dynamic scaling within data pipelines. Our experiments demonstrate significant improvements in processing speed, resource utilization, and reliability. Key Words: Data Engineering, Data Pipelines, Machine Learning, Predictive Resource Allocation, Anomaly Detection, Dynamic Scaling