The Impact of Generative AI on Data Engineering: A Technical Perspective

The integration of generative AI is fundamentally transforming the landscape of data engineering, revolutionizing how organizations approach pipeline development, maintenance, and optimization. This transformation spans across various sectors, from automotive to insurance, introducing sophisticated approaches to data processing and management. The article explores how AI-driven solutions are enhancing pipeline efficiency, automating routine tasks, and enabling more sophisticated approaches to data quality management and monitoring. Through case studies in automotive and insurance sectors, the research demonstrates the practical implementation of AI-powered systems in real-world scenarios, highlighting advancements in areas such as real-time data processing, automated schema translation, and intelligent metadata management. The article also examines technical implementation considerations, emphasizing the importance of robust frameworks for AI integration and performance monitoring.

Authors

Publication Details

Journal
International Journal on Science and Technology
Published
2025-03-21
DOI
https://doi.org/10.71097/ijsat.v16.i1.2497
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Impact of Generative AI on Data Engineering: A Technical Perspective

Raghavendra Kurva -
International Journal on Science and Technology
Big Data and Business Intelligence
article

The Impact of Generative AI on Data Engineering: A Technical Perspective

Raghavendra Kurva -
article en

Abstract

The integration of generative AI is fundamentally transforming the landscape of data engineering, revolutionizing how organizations approach pipeline development, maintenance, and optimization. This transformation spans across various sectors, from automotive to insurance, introducing sophisticated approaches to data processing and management. The article explores how AI-driven solutions are enhancing pipeline efficiency, automating routine tasks, and enabling more sophisticated approaches to data quality management and monitoring. Through case studies in automotive and insurance sectors, the research demonstrates the practical implementation of AI-powered systems in real-world scenarios, highlighting advancements in areas such as real-time data processing, automated schema translation, and intelligent metadata management. The article also examines technical implementation considerations, emphasizing the importance of robust frameworks for AI integration and performance monitoring.

International Journal on Science and TechnologyVol. 16(1)
Openalex Percentile: Top 91%
Big Data and Business Intelligence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

The Impact of Generative AI on Data Engineering: A Technical Perspective — Raghavendra Kurva - · International Journal on Science and Technology (2025) | TGRS Research Map | TGRS