AI-Augmented Data Engineering Pipelines for Real-Time Analytics in HighDimensional Datasets

Abstract

The exponential growth of high-dimensional data in various domains necessitates efficient and scalable data engineering pipelines capable of handling real-time analytics. Traditional data processing architectures struggle to maintain performance, scalability, and accuracy in dynamic environments with massive data streams. This study proposes an AIaugmented data engineering pipeline that leverages machine learning-based feature selection, automated ETL processes, and real-time anomaly detection to enhance data processing efficiency in high-dimensional datasets. The framework integrates deep learning models, reinforcement learning-based resource allocation, and distributed computing to optimize data ingestion, transformation, and storage while ensuring low-latency analytics. Experimental evaluations demonstrate that the AI-powered pipeline outperforms traditional approaches by reducing data processing time by 46%, improving feature selection accuracy by 38%, and lowering computational costs by 42%. The proposed system significantly enhances real-time decisionmaking in critical applications, including finance, healthcare, and IoT-driven smart environments. This study underscores the role of AI in revolutionizing modern data engineering pipelines, paving the way for adaptive, intelligent, and scalable data analytics frameworks.