So, I'm not using it currently, but when I was working with my last organization, which was a gaming company, we used Amazon Neptune for real-time fraud detection. We maintained user data, mobile device data, and other information as graph data. These vertices and the connections between them (edges) were stored along with properties. A real-time pipeline was used to input data into the Neptune database, and several data science jobs triggered every minute or so for real-time fraud detection.
In my organization, we need to maintain hierarchies of employees and customers. We also have to track the history of these hierarchies, which is why we are using a graph database. Our earlier choice was Neo4j, but due to cost issues, we switched to Neptune.
Senior Database Administrator at Summa Health System
Real User
2022-03-30T18:41:00Z
Mar 30, 2022
The Amazon Neptune is a graph database. In the graph database, instead of relational tables, you have nodes and edges. For example, in a conversation between two people, the two people would be a node, and they are connecting because they are talking and that would be an edge. If you can imagine the model spreading out, this person knows this person, this person works with this person. It's more of a LinkedIn or Facebook-type relationship, which has its benefits.
Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Amazon Neptune supports popular graph models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin and SPARQL,...
So, I'm not using it currently, but when I was working with my last organization, which was a gaming company, we used Amazon Neptune for real-time fraud detection. We maintained user data, mobile device data, and other information as graph data. These vertices and the connections between them (edges) were stored along with properties. A real-time pipeline was used to input data into the Neptune database, and several data science jobs triggered every minute or so for real-time fraud detection.
In my organization, we need to maintain hierarchies of employees and customers. We also have to track the history of these hierarchies, which is why we are using a graph database. Our earlier choice was Neo4j, but due to cost issues, we switched to Neptune.
The Amazon Neptune is a graph database. In the graph database, instead of relational tables, you have nodes and edges. For example, in a conversation between two people, the two people would be a node, and they are connecting because they are talking and that would be an edge. If you can imagine the model spreading out, this person knows this person, this person works with this person. It's more of a LinkedIn or Facebook-type relationship, which has its benefits.