Knowledge Graph enables storing data in a graph model and using graph queries to intuitively explore highly connected datasets. Another strong graph use case is a Knowledge Graph. However, they recently gained traction because they provide a good solution for use cases such as social networking, recommendation systems and fraud detection. In fact, they were already introduced 20 years ago, in the early 2000s. The graph is the element that links together the data in “relationships”, and then retrieves them, from nodes, edges, and properties. What is a Graph Database?Ī graph database (sometimes referred to as GDB or GraphDB) is a database that uses graph structures to represent and store data, enabling semantic queries of the data points. This blog post will compare the four graph databases that we tried: AWS Neptune, Neo4J, ArangoDB and RedisGraph, and explain why we eventually chose ArangoDB for our needs.īut first, let’s understand what a Graph Database is. In the process, we experimented with several graph databases, until we found the one that fit us the most. We needed these capabilities for our own internal use and also as a solution for giving our customers a flexible way to query their data. Here at Cycode, we were searching for a graph database to organize our data and to enable complex detections and insights. In traditional relational SQL databases, this would be typically modelled as a large number of tables. Such databases excel at querying data that is related to each other via a long chain of connections. Graph databases are a kind of database that uses graph structure for semantic queries on nodes and edges, as well as properties to model and persist data.
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