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This feature is currently in public preview. This preview is provided without a service-level agreement, and isn't recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.
A Labeled Property Graph (LPG) is a data model that represents entities as nodes and connections as edges, with labels and properties on both. Graph in Microsoft Fabric uses the LPG model to deliver fast traversal and query performance for analytics and connected data.
What is a labeled property graph (LPG)?
Many popular graph databases use the LPG data model, including graph in Microsoft Fabric. In an LPG:
- You represent data as nodes and edges, which are also sometimes called vertices and relationships, respectively.
- You classify nodes (such as
PersonorProduct) and edges (such asFRIENDS_WITHorPURCHASED) with labels. - Both nodes and edges can have properties—key-value pairs that store more data (such as
{name: "Alice", age: 30}for a node,{since: 2020}for an edge).
LPGs don't require global identifiers such as Internationalized Resource Identifiers (IRIs) or Uniform Resource Identifiers (URIs) for every node or edge. Instead, they use internal or application-level identifiers. Your application defines the meaning of labels.
Resource Description Framework (RDF) comparison
Important
Graph in Microsoft Fabric only supports the LPG model. Resource Description Framework (RDF) isn't supported.
RDF is a World Wide Web Consortium (W3C)-standardized model for representing information as subject-predicate-object triples. It's often used for semantic web and knowledge graph scenarios. RDF excels at interoperability, data integration, and formal reasoning with ontologies. However, graph doesn't support RDF.
If your use case requires semantic web standards, semantic web ontologies, or global data integration, consider other platforms that support RDF. For enterprise analytics, operational graph workloads, and business intelligence scenarios, LPG is the recommended and supported model.
Key benefits of the LPG model in Fabric
For most customers, LPG provides the best balance of performance, usability, and integration for connected data analytics in Microsoft Fabric.
- Simplicity and intuitiveness: Nodes and edges map closely to how people think about networks. LPG is less complex than RDF. You don't need to define ontologies or manage global identifiers.
- Properties on edges: Model weighted, temporal, or labeled relationships on edges. This feature supports advanced analytics like recommendations and fraud detection.
- Performance and storage efficiency: LPG-based graph databases store data compactly and enable fast traversals, even for large, complex graphs.
- Flexible schema: Evolve your graph model as your business needs change, without rigid constraints.
- Integration with Fabric: Graph works with OneLake and Power BI, enabling seamless analytics and visualization.