Knowledge Graphs for Infrastructure Data Explained

Network automation has historically lagged in data management innovations, but the adoption of knowledge graphs from other industries could revolutionize it. Knowledge graphs, used for over a decade in fields like retail and finance, represent entities and their relationships as nodes and edges, providing meaningful context and facilitating reasoning, inference, and automation. Unlike traditional relational models that struggle with complex, interconnected infrastructure data, knowledge graphs offer a scalable solution by natively storing relationships. This is enhanced by combining graph databases for storage and query capabilities with domain-specific schemas that define and validate data structure and ontology. Such a system not only manages infrastructure data effectively but also becomes AI-ready, enabling agents to traverse and reason about the data efficiently. Products like Infrahub leverage this model, offering platforms that not only store but understand infrastructure data, providing insights into current states and potential impacts of changes, marking a significant shift in how infrastructure automation can be approached.