Modernizing Data Infrastructure: How AI and ML are Transforming SQL and NoSQL Usage in Distributed Manufacturing

Abstract

In the era of Industry 4.0, manufacturing systems are increasingly adopting distributed architectures that demand agile, scalable, and intelligent data infrastructures. Traditional SQL and emerging NoSQL databases are being reshaped by the integration of Artificial Intelligence (AI) and Machine Learning (ML) to optimize data storage, query efficiency, and real-time decision-making. This paper explores how AI and ML are transforming the design, deployment, and operation of SQL and NoSQL systems in distributed manufacturing environments. We investigate architectural evolutions, automation of query tuning, predictive indexing, anomaly detection in sensor data, and adaptive data partitioning. Through a detailed review and technical study, we highlight practical frameworks and implementations that bridge data systems with intelligent agents. Real world case studies illustrate successful deployments in smart factories, emphasizing the impact of intelligent data systems on productivity and agility. Our findings provide insights into future trends, challenges, and opportunities in modernizing data infrastructure using intelligent technologies. The SQL and NoSQL systems are being revolutionized by AI and ML in manufacturing to enhance data handling, query efficiency, and decision-making. SQL queries and indexing are optimized by AI, whereas NoSQL has AI-based partitioning and schema management. AI/ML helps with predictive maintenance, identifies anomalies, and supports automation. In this paper, we will discuss the use of AI/ML to modernize data infrastructure with Industry 4.0 in mind, smart factory examples, and comment on the future of the topic.