Database and Modern Database Technology

Abstract

This paper articulates a holistic approach to study the future of database development as well as the interaction between AI and modern database technologies. Overall architecture is data-centric so that quality of data, security, and governance are enhanced. It addresses the scalability and cost-effectiveness of cloud-native versus serverless database solutions and introduces AI-powered approaches towards the management of databases like predictive maintenance, self-healing, and XAI toward transparency and accountability. Methodology: Real-time Data Analysis Real-time data analysis involving new tools such as Apache Kafka and Spark Streaming coupled with emerging AI techniques - GNNs, NLP, reinforcement learning, transfer learning, and quantum computing. Comparative analysis with Google Cloud AI Platform along with a comparison of its AI tools and platforms and even against another tool like Apache Cassandra that is used to implement such real-world applications, studying their efficiency in it. Finally, the research suggests strategies that will help in future-proofing database management with robust data governance, continuous learning, stakeholder collaboration, and adaptability to evolving technologies. This methodology is designed to draw on theoretical research, experimental validation, and practical case studies to provide a structured framework in which AI can be leveraged to drive innovation and sustainability in database systems.