Enterprise information systems increasingly operate in environments characterised by high data volume, velocity, and variety, driven by cloud computing, microservices architectures, and data-driven decision-making. Database management systems play a critical role in enabling these enterprises to store, process, and analyse both structured and unstructured data efficiently. Traditionally, relational database management systems (RDBMS) have formed the foundation of enterprise data infrastructure due to their strong consistency guarantees, well-defined schemas, and robust transactional support based on ACID principles. However, the limitations of relational databases in horizontally scaling and handling rapidly evolving, large-scale workloads have led to the emergence and widespread adoption of NoSQL databases. NoSQL systems introduce flexible schema models, distributed architectures, and scalable data storage mechanisms that are better suited for real-time analytics, big data processing, and cloud-native applications.

This paper presents a comprehensive comparative review of relational and NoSQL databases within enterprise systems, examining their architectural designs, data models, consistency mechanisms, performance characteristics, and operational trade-offs. The study analyses how relational databases excel in structured, transaction-intensive, and compliance-driven environments, while NoSQL databases offer superior scalability, availability, and flexibility for high-throughput and distributed workloads. Key aspects such as ACID versus BASE properties, horizontal and vertical scalability, query capabilities, fault tolerance, and deployment in hybrid and cloud-native environments are critically evaluated. The review also discusses enterprise deployment considerations, including security, regulatory compliance, integration strategies, and performance evaluation metrics. Furthermore, the paper identifies existing research gaps related to polyglot persistence, hybrid database orchestration, real-world benchmarking, and AI-driven database optimisation. By synthesising current literature and practical deployment insights, this review guides system architects, database administrators, and researchers in selecting and designing database solutions that align with enterprise workload requirements, performance expectations, and long-term scalability goals in modern distributed environments.

Nature presents the world with colossal issues, and human-induced disasters pose immense challenges to properties, people, and the Earth. It can devastate them and cost them tonnes of money, besides leaving the social front all messed up. Over the past few decades, we have observed an increasing number of catastrophes, floods, hurricanes, earthquakes, tsunamis, forest fires, technological accidents, and health epidemics. This demonstrates the fact that having good disaster management plans is very important. 

Disaster management is not simply a question of turning around and speeding supply anywhere so that they can arrive to pick up the lost or transported supply: this requires not only fast availability but also accurate, timely and location agency information such that the leaders can see what, where, when and how to do it. In this regard, GIS has transformed things. It also allows individuals to seize, store, examine and display maps with information to assist in all aspects of disaster responses.