Why do social platforms like Facebook and Twitter use graph databases? As the data generated by Web 2.0 platforms continued to grow in size, relational databases could not process the information fast enough. Graph databases were developed to cope with processing big data.
Graph databases are more similar to object-oriented structures than relational databases. Employing nodes, properties and edges, graph databases do not rely on join operations. Because the schema is less strict, it can handle fluctuating data changes quickly. Graph-like queries, like figuring the shortest path through a series of nodes in a graph, are handled rapidly and naturally.
Distinct from NoSQL and relational databases, a graph database is built for super fast access to complex data sets common to networks, recommendation engines and social media platforms. Traditional databases are tabular. Graph databases present data as it is in reality—a series of separate objects linked by relationships. Programmers can begin to code graph databases instantly.
A basic query can run hundreds of times faster on a graph database than a traditional relational database. Graph databases do not rely on an index. Every element is linked to another by each "edge" coming in and out. Records can be accessed in a fraction of a second.
On the other hand, a relational database first goes through several stages to see how things are linked before it pulls the results of the query. Because graph databases do not rely on an index, they can easily adapt as the database grows to huge levels. In a sense, the data itself becomes the structure that holds it.
Graph databases are becoming more popular for several use cases including cloud management, geospatial applications, bioinformatics, and security control. The main benefit is speed. A basic query can run hundreds of times faster on a graph database than a traditional relational database.