In-Depth Comprehensive Solutions for the Effective Implementation of Hybrid Data Management Architectures in Contemporary Computing Ecosystems

Abstract

This research paper explores innovative approaches to Hybrid Data Management Architectures (HDMAs), which integrate traditional relational databases with modern non-relational systems like NoSQL and NewSQL to address contemporary data challenges. HDMAs aim to provide scalable, flexible, and efficient data management solutions suitable for the diverse and dynamic needs of modern enterprises, driven by big data, cloud computing, and IoT. The study delves into the limitations of traditional data management systems, such as rigid schemas, limited scalability, and high maintenance costs, and investigates cutting-edge technologies like hybrid transactional and analytical processing (HTAP) systems and data virtualization. By analyzing the benefits, such as enhanced flexibility and scalability, and potential limitations, including increased complexity and integration challenges, the research offers valuable insights into optimizing data management strategies. The paper is structured to provide a comprehensive understanding of HDMAs, supported by empirical data, expert opinions, and real-world case studies, highlighting the significance of robust data management frameworks in leveraging AI and ML for transformative business outcomes.