GenAI Data Engineering: Synthetic Data and Feature Engineering framework for Cloud Analytics
Independent Researcher, Wilmington University, Delaware, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(01), 2867-2877
Publication history:
Received on 08 September 2024; revised on 23 October 2024; accepted on 28 October 2024
Abstract:
The integration of generative artificial intelligence into modern data engineering pipelines represents a transformative paradigm shift addressing critical challenges in data scarcity, privacy preservation, and feature engineering automation. Traditional data engineering approaches struggle with rare event representation, imbalanced datasets, privacy-constrained environments, and labor-intensive feature creation processes that limit machine learning model effectiveness and organizational agility. This research presents a comprehensive cloud-native data engineering framework that leverages generative AI technologies including Variational Autoencoders, Generative Adversarial Networks, and diffusion models for synthetic data generation, combined with transformer-based architectures for automated feature engineering and embedding creation. The proposed architecture integrates synthetic data generation capabilities throughout the data lifecycle, from ingestion through storage, feature engineering, model training, and inference, while maintaining comprehensive governance through data quality validation, model drift detection, and regulatory compliance monitoring. Experimental validation across multiple use cases demonstrates that synthetic data augmentation improves model performance by 23.7% for rare event detection, reduces feature engineering effort by 64%, achieves 97.3% statistical fidelity to production data distributions while preserving privacy guarantees, and accelerates model development cycles by 58% through automated feature generation. The framework addresses critical gaps in existing data engineering practices by unifying generative AI capabilities with traditional extract-transform-load pipelines, feature stores, and governance frameworks within a cohesive architecture validated through production deployment processing petabyte-scale datasets. This work contributes both theoretical foundations for generative AI integration in data engineering and practical implementation patterns for organizations seeking to modernize analytics infrastructure while addressing data privacy, quality, and scalability requirements.
Keywords:
Generative AI; Synthetic Data Generation; Feature Engineering; Data Governance; Cloud Analytics; Machine Learning Operations; Privacy-Preserving Analytics
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0