Microsoft Fabric Blog announces a new native graph data management and analytics service in Microsoft Fabric. This service empowers enterprises to harness relationship-rich insights, with features for business users, analysts, data engineers, and developers.

Introducing Graph Data Management and Analytics in Microsoft Fabric

Microsoft Fabric introduces a comprehensive native graph data management, analytics, and visualization service purpose-built for the era of AI and interconnected data. This new offering leverages a horizontally scalable, native graph engine designed to help organizations of any size manage, analyze, and visualize complex, interconnected data with unprecedented flexibility.

The Need for Relationship-First Data

Modern industries—ranging from logistics to banking and e-commerce—face challenges related to complex, deeply interconnected data. Traditional relational models and their reliance on complex joins often fail to provide the agility or performance needed in these multi-domain scenarios. Graph analytics, on the other hand, models entities and relationships as networks, making multi-hop dependencies and hidden patterns much easier to analyze and act upon.

Some example scenarios include:

  • Fraud detection in banking, by modeling connections between accounts, transactions, and merchants
  • Supply chain optimization through multi-hop asset tracking and dependency mapping
  • E-commerce personalization using customer journey and product catalog relationships

Graph analytics enables a 360° view of entities, allowing for faster detection of fraud, smarter product recommendations, and optimized business operations.

Microsoft Fabric’s Native Graph Service

Delivered as an integrated part of Microsoft Fabric and OneLake (Fabric’s unified data lake), the new graph service offers:

  • Horizontally scalable native graph engine for large-scale analytics
  • Unified access to organizational data with no heavy ETL or duplication
  • Rich role-based experiences:
    • Business users: Visual exploration and natural language graph querying
    • Analysts: No/low-code pattern building, customizable diagram and tabular views
    • Data engineers: Low/no-code schema mapping from nodes/edges to OneLake sources, and easy publishing of graph models
    • Developers: Advanced GQL query editor and programmatic integration for apps and AI agents
  • Real-time analytics and explainable AI, grounding generative AI and LLMs with reliable, relationship-driven context

Key Benefits and Use Cases

  • No heavy ETL: Data remains in OneLake—no duplication required
  • Instant insights: Quickly build graphs from sales, marketing, service, operations, and more
  • Multi-hop analysis: Effortlessly trace relationships and uncover hidden clusters and patterns
  • Comprehensive tooling: Supports everyone from business users to advanced developers

Examples of application areas:

  • Fraud rings identification
  • Optimizing supply chain dependencies
  • Customer journey analysis
  • Asset hierarchy mapping

Integrating with AI

Pairing the graph service with Copilot and LLMs (Large Language Models) enhances the explainability, relevance, and precision of AI-generated answers. AI agents and orchestration tools are empowered with richer, contextual knowledge of how data is related, enabling safer automation and more grounded intelligence.

Getting Started and Resources

Graph in Microsoft Fabric begins rolling out regionally in October 2025. For details and updates:

Certifications, learning paths, customer success stories, and training days are also available to help organizations get up to speed with real-time graph analytics in Microsoft Fabric.

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