172. Graph Model を試す

Graph in Microsoft Fabric is a scalable, enterprise-grade solution that turns disconnected data into AI-powered insights.

Unlike traditional relational databases that often require costly joins and complex queries, graph in Microsoft Fabric:

・Introduces a scale-out architecture that implements the flexible labeled property graph model.
・Offers data querying functionality that's compliant with the international standard for the graph query language GQL (ISO/IEC 39075).

Together, these capabilities enable advanced graph analytics directly on OneLake without the need to manually set up brittle ETL (extract, transform, load) or data replication workflows.

Graph in Microsoft Fabric's scale-out design ensures performance and elasticity for enterprise-scale workloads, so you can analyze billions of relationships without bottlenecks. Its labeled property graph model lets nodes and edges carry labels and properties that make complex relationships easy to model, query, and explore.

And with native GQL and NL2GQL support, you gain standards-based querying capabilities optimized for graph operations—delivering portability and consistency across graph solutions. Graph in Microsoft Fabric removes the complexity of joins and transformations to unlock seamless graph analysis and advanced insights at scale, all while using your existing data estate.

・Graph databases: Organize data as nodes and edges, storing relationships explicitly. Traversing connections is efficient and often independent of total dataset size.
・Relational databases: Organize data in tables with rows and columns. Relationships are inferred at query time using JOIN operations, which can become complex and slow for highly connected data.