In today's Big Data world identifying and correlating data points can be extremely challenging yet very important for business success. Different techniques have evolved over time for handling changing nature of data generated by business systems. Graph Theory provides an effective mechanism to identify relationships in such complex data. Graph Databases provides an efficient way to use the graph theory techniques for organizing and analyzing complex and large volumes of data.
Graph database compliments existing technologies. Some of the most prominent graph database are Allegro Graph, ArangoDB,Horton,Neo4J, Infinite Graph , Graph. These graph database use modelling techniques like Property Graph, Triples Model and hyper graph. Adopting graph database/modelling technique for relevant scenario can bring significant performance benefits over other databases. Graph databases will not replace conventional relational databases, but it is worth to consider Graph DB for harnessing the value of the fundamental interconnectedness of everything. In the case of flat or linear data with standard relations, RDBMS should be considered for storage and analysis. If we have tree- like data (with hierarchies) or graph like data (where relations are in all directions) it’s worth to consider Graph Databases. Hence Graph Databases and Relational databases sit side by side always.