Leveraging Graph Databases for Dynamic Relationship Management and Real-World Data Representation
Author(s):
Venkata Naga Sai Kiran Challa
This paper explores the advantages of using graph databases as the primary mode of data storage to represent complex, real-world relationships. Unlike traditional relational databases, graph databases naturally handle dynamic relationships and evolving schemas, making them ideal for applications with deeply nested and frequently changing data. We delve into the fundamental principles of graph databases, including index-free adjacency, native graph storage, and specialized query languages like Cypher and Gremlin. Additionally, we present a practical implementation of a social media platform's data model in both graph and relational databases, highlighting the simplicity and efficiency of graph databases in managing complex relationships. Through a case study involving advertisement recommendations based on user behavior, we demonstrate the superior capabilities of graph databases in handling dynamic relationship updates and providing insights via graph analytics. This paper aims to provide a comprehensive understanding of the practical applications and benefits of graph databases in modern data management
