At the Databricks AI Summit (DAIS), we showed how easy it is to extend your lakehouse with Neo4j. By enriching your Delta Tables with graph intelligence, you can power deeply connected, governed analytics and enterprise-grade AI.
While connecting your data with our new Virtual Graph is just one way to integrate Neo4j and Databricks, Agent Bricks offers another powerful path forward. With Agent Bricks, you can build advanced GraphRAG applications and AI assistants grounded in your company's unique data—all without ever leaving your Databricks environment.
Ready to take the next step?
- Try the New Virtual Graph: Query knowledge graphs directly from your Delta Tables, no data copy, no ETL. Find out more on the Virtual Graph web page and ask us about Early Access.
- Watch Our Pre-Recorded Webinars: Catch up on our latest sessions and grab deep-dive insights into AI.
- Explore AI Resources: Access blueprints, sample code, and implementation guides in our Graph on Databricks repository.
- Neo4j + Databricks: View more details on the Neo4j + Databricks web page.
Fraud rings do not announce themselves in a single row. They hide two or three hops away from the obvious suspects, woven into transactions that look ordinary until you trace the full network. The analyst tools stay the same, what changes is what the data reveals. Your Databricks Lakehouse already holds the data. What it lacks is a way to traverse it as a connected graph. We walk through a live fraud investigation and show how Neo4j turns days of manual analysis into a query Databricks Genie answers in seconds. The hidden patterns do not stay hidden once they become columns. Neo4j scores every account; those scores land in your Lakehouse as ordinary dimensions. Databricks Genie needs no changes; it queries graph scores the same way it queries region or balance. Before enrichment: a flat list. After: accounts at the center of distinct fraud communities. Attendees leave with the notebook and graph data model on GitHub to replicate this on their Delta Lake data.
Retail AI does not earn trust by sounding conversational. It earns trust by remembering context, understanding product relationships, checking live business data, and grounding answers in source documentation. That requires more than a chatbot over a catalog. It requires a connected intelligence layer where product knowledge, customer context, inventory, pricing, documentation, and prior interactions can be reasoned over together.
INTERPOL’s 2026 Global Financial Fraud Threat Assessment puts global fraud losses at $442 billion in 2025, with financial fraud now ranked among the top five global crime threats. INTERPOL describes it as the industrialization of fraud, driven by AI and global criminal coordination. Much of that loss comes from coordinated schemes that existing analytics infrastructure is structurally unable to detect.
Global supply chains have never been more exposed. Tariff shifts, geopolitical disruptions, and pandemic-era shortages have revealed a structural problem that most organizations already suspected but couldn’t see clearly: they know their tier 1 suppliers, but very few know what sits behind them. A component shortage at a tier 3 supplier, two steps removed from direct contact, can halt production before anyone has time to respond.
Genie answers based on what it can query. Flat tables leave out the relationships between customers, transactions, events that make answers actually useful.
Neo4j enriches your Delta Lake data with graph insights (community labels, connection scores, relationship signals) written back as ordinary Databricks dimensions. Genie gets the context. You get better answers.
See it live in a financial crime investigation demo.
AI startups are building with graph databases because AI needs well-managed context, not incoherent collections of data and tools. We’re investing in the future of AI with the Neo4j Startup Program, giving you the resources to build explainable, scalable, and production-ready applications.
Apply now for up to $16,000 in free Aura credits to use on our fully-managed cloud offering, technical consultations with graph experts, and go-to-market opportunities.
In this hands-on guide, Neo4j’s Jesús Barrasa and Jim Webber show data scientists and engineers how to build and apply knowledge graphs to solve today’s most complex knowledge management challenges. Through practical examples and common design patterns, readers learn how to create knowledge graphs that grow in value with more data, enhanced by algorithms and machine learning for deeper insight and intelligence.
