Both relational databases and some NoSQL databases like the already introduced key-value stores are not efficient when dealing with highly connected data. They lack the performance and the flexibility needed to process and query multiple relationships inside large datasets
Even though that MapReduce paradigm paired to Hadoop framework provide scalability, fault tolerance and easy-to-program tools for large datasets, it has been proven that some NoSQL databases like key-value stores are not always suitable for connected data and very large graphs
On the contrary, graph databases are suitable to store not only information about objects but also all relationships that exist among them. Indeed, graph databases are designed to better manage heavily linked data. Thus, they are suitable for applications with many relationships among their data.
They rely on schema-free graph model that is based on a graph abstraction. This allows users to easily model and represent connectivity. More precisely, such graph model includes a collection of vertices (e.g., objects or items represented by nodes) and edges to represent links, connections or relationships between data. So, graph databases are suitable to capture relations between entities.
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