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Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it.
All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks.
In September 2019, Graph Query Language is accepted as a new database query language standard in a vote by the International SQL Standards Committee, the unification of GQL takes time.
In this post, we've selected some mainstream graph query languages and compared the CRUD usage in these languages respectively.
Like a complex system grid or an air-traffic-control map, a graph database is represented as a network of nodes and connections called a labeled property graph. The nodes, which appear as circles or squares, represent entities such as people, products, companies or orders.
In Neo4j, the connections between database nodes are called relationships, and those relationships are as important as the nodes they connect.