Artificial Intelligence in Big Data Engineering: Architectures and Algorithms for Efficient Data Handling

Abstract

The exponential growth of big data has necessitated the development of advanced engineering 
solutions to manage and process vast volumes of information efficiently. Artificial Intelligence 
(AI) has emerged as a transformative technology in this domain, offering innovative approaches 
to data handling through sophisticated architectures and algorithms. This paper explores the 
integration of AI into big data engineering, focusing on the architectural frameworks and 
algorithms that enhance data processing efficiency and effectiveness. We examine key AI-driven 
techniques, including machine learning, deep learning, and reinforcement learning, and their 
applications in optimizing data storage, retrieval, and analysis. Additionally, the paper discusses 
the impact of AI on improving scalability, reducing latency, and enhancing data quality. Through 
case studies and empirical analyses, we highlight the practical benefits and challenges of 
implementing AI in big data environments. The findings underscore AI's role in addressing the 
complexities of big data and propose future directions for research and development in this 
evolving field.