International Journal of Science, Engineering and TechnologyAuthors: Miss. M. Saranya
Abstract: Traditional relational databases frequently find it difficult to effectively describe, store, and query rich relationship-centric data in an era characterized by increasingly complex and interrelated data. A persuasive answer to this problem has been provided by graph databases, which provide a data model in which entities are represented as nodes and relationships as edges. This allows for high-performance traversal of links and intuitive representation. The use of graph databases to simulate intricate interactions in a variety of fields, including fraud detection, biological networks, recommendation systems, and social networks, is examined in this research. By treating relationships as first-class citizens, graph databases provide smooth relationship queries, even at scale, in contrast to relational databases that depend on expensive joins. The paper illustrates how graph databases improve query performance, simplify schema evolution, and enhance data insight through comparative research, real-world case studies, and schema design examples. We also look at use scenarios where graph-based models perform better than conventional relational systems and talk about important tools like Neo4j, Amazon Neptune, and Apache TinkerPop. The purpose of this presentation is to demonstrate the useful benefits of graph databases and their revolutionary role in releasing the potential of linked data.
