The Future of Data Engineering: AI and Machine Learning Integration

This article examines the transformative impact of artificial intelligence and machine learning integration in data engineering. The article explores various dimensions including automated data processing, intelligent pipeline management, advanced data quality monitoring, and smart governance systems. Through multiple case studies and research findings, the article demonstrates how AI-driven solutions have revolutionized traditional data engineering practices, from automated feature engineering in healthcare analytics to enhance security measures in cloud environments. The research highlights significant improvements in processing efficiency, data quality management, and decision-making capabilities across organizations implementing AI-powered systems, while also examining the role of MLOps practices and natural language processing in modernizing data operations.

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Publication Details

Journal
European Journal of Computer Science and Information Technology
Published
2025-04-15
DOI
https://doi.org/10.37745/ejcsit.2013/vol13n117986
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

The Future of Data Engineering: AI and Machine Learning Integration

Gopinath Govindarajan
European Journal of Computer Science and Information Technology
Big Data and Business Intelligence
article

The Future of Data Engineering: AI and Machine Learning Integration

Gopinath Govindarajan
article en

Abstract

This article examines the transformative impact of artificial intelligence and machine learning integration in data engineering. The article explores various dimensions including automated data processing, intelligent pipeline management, advanced data quality monitoring, and smart governance systems. Through multiple case studies and research findings, the article demonstrates how AI-driven solutions have revolutionized traditional data engineering practices, from automated feature engineering in healthcare analytics to enhance security measures in cloud environments. The research highlights significant improvements in processing efficiency, data quality management, and decision-making capabilities across organizations implementing AI-powered systems, while also examining the role of MLOps practices and natural language processing in modernizing data operations.

European Journal of Computer Science and Information TechnologyVol. 13(11)
University of Madras (IN)
Openalex Percentile: Top 85%
Big Data and Business Intelligence
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