My main use case for ArangoGraph is to build a bridge between us and the client to showcase applications such as social networks, recommendation engines, fraud detection, network and dependency analysis, knowledge graphs, and graph RAG applications, as well as supply chain and logistics tasks. I use the enterprise edition, which provides community and open-source options. Out of all those use cases, the RAG solution is the project that stands out to me as especially interesting and challenging, as it provides the highest impact. This is currently one of the hottest use cases. Imagine an enterprise chatbot that, instead of storing documents in isolated chunks, works with AI to understand who owns a service, which projects depend on it, who the manager is, and what incidents have occurred recently. These things can be easily tracked from ArangoGraph tools. Instead of storing documents separately, we create a knowledge graph that maps employees to projects, applications to services they depend on, and everything else properly. This delivers significant impact on enterprise-grade AI assistants and graphical RAG orchestration and architecture.
ArangoGraph's best use case is relationship mapping, such as finding connections between entities like which user interacted with which product through which channels. Graph traversal queries make this extremely fast and intuitive. ArangoGraph changed the way our teams think about data. Instead of thinking in tables and rows, we started thinking in relationships and connections. This mental shift improved our overall data modeling approach across the entire project. The graph traversal features were transformative; finding second and third degree relationships between entities that would have required multiple complex SQL joins was simply solved with a straightforward AQL query in ArangoGraph. This directly improved our application's recommendation logic performance.
My main use case for ArangoGraph is to build a customer graph in order to create a relation between customer and end users. I connect all the user related data together between the orders that they made from the supplier and customers.
ArangoGraph is a highly flexible multi-model database that efficiently handles graph, document, and key-value pairs, supporting diverse data structures within a single comprehensive platform.ArangoGraph offers seamless functionality for complex queries and provides significant utility for data professionals seeking to integrate data models without data migration. It helps solve intricate data handling challenges across different use cases through its integrated approach that prioritizes query...
My main use case for ArangoGraph is to build a bridge between us and the client to showcase applications such as social networks, recommendation engines, fraud detection, network and dependency analysis, knowledge graphs, and graph RAG applications, as well as supply chain and logistics tasks. I use the enterprise edition, which provides community and open-source options. Out of all those use cases, the RAG solution is the project that stands out to me as especially interesting and challenging, as it provides the highest impact. This is currently one of the hottest use cases. Imagine an enterprise chatbot that, instead of storing documents in isolated chunks, works with AI to understand who owns a service, which projects depend on it, who the manager is, and what incidents have occurred recently. These things can be easily tracked from ArangoGraph tools. Instead of storing documents separately, we create a knowledge graph that maps employees to projects, applications to services they depend on, and everything else properly. This delivers significant impact on enterprise-grade AI assistants and graphical RAG orchestration and architecture.
ArangoGraph's best use case is relationship mapping, such as finding connections between entities like which user interacted with which product through which channels. Graph traversal queries make this extremely fast and intuitive. ArangoGraph changed the way our teams think about data. Instead of thinking in tables and rows, we started thinking in relationships and connections. This mental shift improved our overall data modeling approach across the entire project. The graph traversal features were transformative; finding second and third degree relationships between entities that would have required multiple complex SQL joins was simply solved with a straightforward AQL query in ArangoGraph. This directly improved our application's recommendation logic performance.
My main use case for ArangoGraph is to build a customer graph in order to create a relation between customer and end users. I connect all the user related data together between the orders that they made from the supplier and customers.