International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 11 | Nov 2024
p-ISSN: 2395-0072
www.irjet.net
Impact of Generative AI on Data Engineering Ajay Krishnan Prabhakaran Data Engineer, Meta Inc ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Generative AI, a rapidly evolving branch of
artificial intelligence, has emerged as a transformative force in the field of data engineering. By automating data pipeline creation, generating synthetic data, and improving data quality, generative AI is reshaping how organizations handle large-scale data. This paper explores the theoretical underpinnings of generative AI, its applications in data engineering, and real-world case studies that demonstrate its potential. Furthermore, it addresses the limitations and challenges associated with generative AI, including biases, computational costs, and ethical concerns. Finally, the paper outlines future research directions to enhance the adoption and efficiency of generative AI in the data engineering domain.
Fig -1: Evolution of data engineering with AI
2. GENERATIVE AI: AN OVERVIEW
Key Words: Generative AI, data engineering, automation, synthetic data, ETL pipelines, anomaly detection, machine learning, scalability, data governance, artificial intelligence
2.1 Definition and Key Concepts Generative AI refers to models and algorithms designed to create new, realistic data points or outputs based on patterns learned from existing data. Unlike traditional machine learning, which often focuses on prediction or classification, generative AI creates new instances, making it especially valuable in scenarios where data availability or quality is a concern.
1.INTRODUCTION Data engineering forms the backbone of modern data-driven enterprises. It involves designing and building systems that collect, store, and analyze vast amounts of data efficiently. With the exponential growth in data volumes, traditional data engineering methods face limitations in scalability, cost, and efficiency. Enter Generative AI, a subfield of AI focused on creating new data and outputs, which offers innovative solutions to these challenges.
2.2 Core Technologies
Generative AI models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based architectures (e.g., GPT-4), can augment human capabilities by automating repetitive tasks, generating high-quality synthetic data, and improving data pipeline operations. This paper aims to explore the profound impact of generative AI on data engineering by addressing three key areas:
How generative AI optimizes data engineering processes
Real-world applications and use cases
Challenges and potential research directions
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Impact Factor value: 8.315
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Generative Adversarial Networks (GANs): GANs consist of two neural networks—the generator and discriminator—that compete against each other. The generator creates data, while the discriminator evaluates its authenticity. Over time, the generator produces increasingly realistic outputs. Applications: Synthetic data generation, anomaly detection
Variational Autoencoders (VAEs): VAEs focus on learning latent representations of data and reconstructing it to generate new samples. Applications: Filling in missing data, augmenting datasets for machine learning
Transformer-Based Models (e.g., GPT-4): Transformers process sequential data, excelling in generating text, code, and structured data representations. Applications: Automating pipeline creation, query optimization
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