From Lakehouse to Connected Knowledge

Enterprises rely on Databricks for scalable, governed data and AI. Neo4j adds graph intelligence to turn that data into knowledge and context, grounding agentic AI systems in trusted relationships and meaning. 

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Why Graph Intelligence Matters for the Databricks Lakehouse

Databricks excels at large-scale data engineering, analytics, and AI – but most real-world problems depend on understanding how data is connected. Neo4j adds graph intelligence to the Databricks Lakehouse,

Databricks Provides Scale 

Databricks unifies data engineering, analytics, and AI on an open lakehouse architecture optimized for scale, performance, and governance.

Neo4j Adds Understanding

Neo4j models relationships, ontologies, and meaning – turning disconnected records into a living knowledge graph that supports reasoning, explainability, and continuous enrichment.

Together: Knowledge-Driven Outcomes 

Neo4j and Datbricks together enable moving from data processing to knowledge-driven decision-making—powering advanced analytics, GraphRAG, and explainable AI.

Connected Knowledge

Turn Lakehouse Data into Connected AI Context

Databricks customers extend the lakehouse with Neo4j graph intelligence, moving data from Delta Tables into knowledge graphs enriched with relationships for governed analytics and AI. With Agent Bricks, they build GraphRAG apps and assistants grounded in connected enterprise data.

How Neo4j and Databricks Work Together

Neo4j and Databricks bring graph intelligence to the lakehouse without compromising governance, security, or performance. Open integrations let teams move data, query across systems, and build AI grounded in trusted relationship context within existing workflows.

Neo4j Spark Connector

Bi-directional bridge between the Databricks Lakehouse and Neo4j knowledge graphs

Write Lakehouse rows to Neo4j as nodes
and relationships

Pull graph data into Databricks as Spark DataFrames

Works natively within Spark notebooks and workflows

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Neo4j MCP Server

Implements the Model Context Protocol (MCP) for AI agent tool use

Enables Databricks AI agents to connect to Neo4j and discover graph schemas

Allows agents to run Cypher queries autonomously

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GraphRAG with
Databricks Foundation Models

Combine vector search with graph context for more accurate AI

Generate embeddings using Databricks Foundation Models

Store document chunks as graph nodes linked to domain entities

Retrieve answers grounded in text similarity and connected knowledge

Use Cases

Solving Relationship-Driven Problems Across Industries

The most valuable Databricks workloads involve complex relationships across people, transactions, systems, and time. Neo4j enables these relationship-driven use cases at scale.

Fraud & Financial Crime

Expose fraud rings, mule networks, and risk propagation paths that relational queries struggle to identify.

How Gilead Sciences made it work

Customer 360 & Personalization

Build real-time customer knowledge graphs to drive segmentation, recommendations, and engagement. 

Use Cases

Supply Chain & Operations

Map dependencies across suppliers, assets, and processes to detect risk and optimize resilience.

How Comcast built a digital twin of its network

Healthcare & Life Sciences

Connect clinical data, trials, and ontologies to accelerate research and ground AI in trusted context. 

How Novo Nordisk accelerates clinical trials

GenAI & AI Applications

Ground Databricks Agent Bricks in connected knowledge using GraphRAG to reduce hallucinations and deliver explainable AI.

How Intuit uses AI agents on a Neo4j graph

Customer Stories

Neo4j and Databricks Share 200+ Mutual Customers

Intuit safeguards its network infrastructure and the data of 100 million customers with Neo4j and Databricks.

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Dun & Bradstreet accelerates complex company ownership checks with Neo4j and Databricks.

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Gilead Sciences combats $431 billion pharmaceutical fraud threat with Neo4j Graph Analytics and Databricks.

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Intuit safeguards its network infrastructure and the data of 100 million customers with Neo4j and Databricks.

Resources

Extend your Databricks lakehouse with graph-powered insights and connected knowledge.

Explore how Neo4j helps Databricks customers uncover relationships, generate insight, and build AI they can trust.

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