Optimizing Big Data Workflows with Machine Learning: A Framework for Intelligent Data Engineering

Abstract

Big Data analytics holds utmost importance for deriving useful insights in most sectors. Traditional data engineering activities typically lag in scalability, effectiveness, and flexibility when handling big and complex datasets. This paper introduces an ML-based framework for enhancing Big Data workflows to tackle major challenges concerning data preprocessing, feature creation, real-time processing, and task planning.
The proposed architecture leverages ML models to control the optimal operation of data pipelines by automating transformation, anomaly detection, and making intelligent choices. With advanced ML algorithms, the system optimizes data ingestion to the maximum, as well as features choice optimization and dynamic resource allocation to maximize big data processing.
New tools and techniques are analyzed to examine how ML will transform traditional data engineering workarounds. By extrapolating actual case studies, we present evidence of how ML-augmented workflows manifest into quicker data processing velocity, precision, and overall system performance. We also examine automation's role in reducing human interferences and operational expense and thus producing agile and resilient Big Data systems.
Finally, we offer future research and industry adoption recommendations, with an observation of trends in ML-based data engineering. Through this research, we aim to provide insights into how organizations can leverage ML to develop more intelligent and scalable data processing frameworks for more efficient Big Data solution automation.