In a hyperconnected, data-rich world, businesses need to understand complex relationships within large, diverse datasets. For example, customer interactions involve tracking behaviors, preferences, and patterns across online platforms, physical stores, and social media. Companies must comprehend all these relationships to optimize strategies and enhance customer experiences.

To address this, Databricks and Neo4j have jointly introduced a validated partner solution. This connector enables their mutual customers to seamlessly integrate structured and unstructured data, uncover hidden patterns across billions of data connections, enhance contextual understanding, and rapidly deliver enterprise-grade GenAI applications.

Neo4j helps businesses efficiently analyze relationships in highly interconnected data, even as volumes grow. Its applications span fraud detection, supply chain and logistics, energy solutions, customer 360, and more.

Developers using Neo4j with Databricks can now:

• Enhance analytics by ingesting data from Databricks to Neo4j: Create a seamless workflow to continuously process, analyze, and update data across both platforms, enabling real-time insights and decision-making.
• Discover hidden patterns for deeper insights: Leverage Neo4j’s built-in graph algorithms and Cypher query language to uncover hidden patterns. In Databricks notebooks, Neo4j Bloom and the Neo4j Visualization Library (NVL) enable visual data exploration.
• Combine Neo4j knowledge graphs with Graph Retrieval-Augmented Generation (GraphRAG): Neo4j’s knowledge graphs enhance RAG by addressing accuracy, explainability, and transparency, unlocking GenAI’s full potential.

Next, let’s explore how Neo4j and Databricks deliver breakthrough analytics and GenAI outcomes.

Data from Databricks to Neo4j for Enhanced Analytics

The Neo4j connector for Databricks seamlessly transfers data to Neo4j for analysis in a graph structure. Neo4j’s graph database excels at handling interconnected data, making it ideal for analyzing complex relationships. The connector can read from and write to Delta tables in Databricks notebooks.

A code snippet demonstrates how the connector ingests data into Neo4j to create nodes, labels, properties, and relationships.

Both Delta Lake and Neo4j are ACID-compliant systems, ensuring data consistency, reliability, and integrity across the pipeline. Delta Lake, an open-source storage layer, brings ACID transactions to Apache Spark and big data workloads. Neo4j and Delta Lake efficiently handle complex queries and real-time insights, scaling to petabytes of data while ensuring consistency through ACID transactions and optimizing reads/writes via indexing and caching.

The Databricks connector prioritizes data availability and security. Neo4j Aura offers a 99.95% uptime SLA for real-time applications and complies with ISO 27001, GDPR, CCPA, SOC2, and HIPAA. Access controls via Databricks Unity Catalog ensure only approved data is analyzed, while Neo4j integrates with SSO providers like Microsoft Azure AD and Okta, offering static encryption via customer-managed keys (CMK) and role-based access control (RBAC).

Discovering Hidden Patterns for Deeper Insights

Neo4j’s developer-friendly schema simplifies prototyping and evolving data models from development to production. Its property graph model stores attributes directly in the graph, streamlining design and implementation.

Graphs built on Neo4j unify transactional, organizational, and vector-embedded data in a single database, simplifying application architecture. Native graph databases enable rapid traversal of connections without costly joins or index lookups—a capability called index-free adjacency, where nodes directly reference neighbors via memory pointers. This ensures processing time scales with data volume, not relationship complexity.

Developers can also use prebuilt graph algorithms and Cypher queries for pattern detection. Centrality, pathfinding, similarity, and other Neo4j algorithms power recommendation engines, supply chain optimization, identity management, and network monitoring.

Knowledge Graphs and GraphRAG: Unlocking GenAI’s Potential

The importance of knowledge graphs in GenAI cannot be overstated. Gartner highlights their critical role, urging data leaders to “harness LLMs’ power and knowledge graphs’ robustness to build fault-tolerant AI applications.”

As GenAI adoption grows, knowledge graphs excel at improving LLM accuracy, relevance, and transparency. They contextualize responses by representing relationships and integrating structured/unstructured data.

With GraphRAG, LLMs retrieve relevant information from knowledge graphs using vector and semantic searches, then augment responses with contextual data. Microsoft researchers found GraphRAG-powered LLMs deliver more comprehensive, interpretable, and diverse answers.

Databricks developers can accelerate GenAI projects by integrating GraphRAG capabilities. Ready-made integrations provide easy access to AI frameworks like LangChain and LlamaIndex.

Adding Critical Analytics and GenAI Capabilities to Databricks

Extracting insights from densely connected datasets and accelerating GenAI development are top priorities for modern enterprises. The Neo4j-Databricks integration helps organizations tackle these challenges and stay ahead in the GenAI and analytics landscape.

Neo4j’s graph database platform empowers businesses to uncover hidden relationships across billions of connections, solving critical challenges in fraud detection, customer 360, supply chains, IoT, and more.

The post The Neo4j + Databricks connector delivers deeper insights and accelerates GenAI development first appeared on DKM Ecosystem.

BNP Paribas Personal Finance specializes in retail financing through consumer credit and installment payment services, with fraud prevention being a top priority. As a wholly-owned subsidiary of BNP Paribas Group and a leader in European personal finance, it serves customers, partners, and employees across 33 countries.

While installment services allow consumers to access future funds conveniently, they also attract fraudsters who manipulate applications to conceal identities and avoid repayment. Mehdi Barchouchi, Head of Innovative Data and Tools for Risk at BNP Paribas Personal Finance France, explains: “Fraudsters reuse information across applications and alter details to bypass rules and blacklists.”

With over 800,000 applications and trust from 85 retailers, robust anti-fraud tools are critical. The company adopted Neo4j’s graph database after a proof of concept (POC) to enhance its fraud detection system, overcoming fraudsters’ sophisticated data manipulation strategies.

Close Collaboration with Neo4j Paved the Way for Success

Relational databases struggled with real-time analysis of interconnected data. Barchouchi notes: “We needed to link credit applications with diverse data—even without shared identifiers—to enable instant risk scoring.” Traditional SQL databases couldn’t efficiently traverse deep relationships, leading to performance bottlenecks.

BNP Paribas chose Neo4j’s enterprise graph database, with Neo4j’s team assisting in data modeling and query development. The result? A 20% reduction in fraud.

Graph-Powered Fraud Detection Cuts Fraud by 20%

The new system processes applications with a maximum 2-second delay, integrating real-time data to identify fraud patterns via graph-based similarity links. Julie Cavarroc, Data Scientist at BNPP PF’s Central Risk Scoring Hub, highlights: “Neo4j provides broader context, revealing complex fraud networks without excessive false rejections.”

The solution rejects only a small fraction of applications while significantly reducing fraud—demonstrating precision without compromising loan volume.

A Winning Partnership

BNP Paribas continues refining its graph models to adapt to evolving fraud tactics. Barchouchi affirms: “Our legacy database couldn’t deliver these results. Neo4j enabled a fraud detection system that exceeded expectations.”

Neo4j’s graph database platform helps businesses uncover hidden relationships across billions of connections, driving solutions for fraud detection, customer 360, supply chains, IoT, and more.

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With Neo4j’s GraphRAG ecosystem tools, you can easily get started with knowledge graph-based GenAI applications, improving response quality and interpretability while accelerating development and adoption.

GraphRAG combines retrieval-augmented generation (RAG) with knowledge graphs to address key LLM challenges like hallucinations and lack of domain-specific context. Unlike traditional RAG solutions that only provide access to fragmented text data, GraphRAG integrates structured and semi-structured information into the retrieval process.

Knowledge graphs provide contextual memory, enabling LLMs to answer questions reliably and act as trusted agents in complex workflows. GraphRAG helps users create knowledge graphs from unstructured text and leverages them—or existing graph databases—to retrieve relevant information for generative tasks using vector and graph searches.

Key Tools

Knowledge Graph Builder: Quickly transforms unstructured text (PDFs, Word docs, YouTube transcripts, Wikipedia pages, etc.) into structured graphs, revealing hidden entities and relationships.

Frontend: A React app using Neo4j’s design system and visualization library.

Backend: Python-based (FastAPI) with LangChain integration, running on Google Cloud Run.

NeoConverse: Translates natural language queries into Cypher for graph-based responses. Workflow:

User selects a dataset and response format (text/graph).

The system extracts the database schema, combines it with the query, and generates a Cypher query via LLM.

Results are validated and used to generate a response.

GenAI Framework Integrations:

Supports Python, JavaScript, and Java.

Works with LangChain (vector/graph search, text-to-graph, advanced RAG), LlamaIndex (Cypher/vector search, knowledge graph construction), Spring AI, and DSPy.

Benefits

Enhanced Accuracy: Reduces LLM hallucinations with structured context.

Faster Development: Pre-built tools for quick integration or customization.

Scalability: Handles billions of data connections for fraud detection, customer 360, IoT, and more.

Explore Neo4j’s GenAI ecosystem for embeddings, vector search, and cloud-native integrations (Google Vertex AI, AWS Bedrock, Azure OpenAI). The platform unlocks hidden patterns across industries, delivering actionable insights.

The post Kickstart your GenAI development effortlessly with Neo4j’s ecosystem tool, GraphRAG! first appeared on DKM Ecosystem.

Intuit, a renowned financial and tax software company founded in 1983, specializes in providing financial management, tax management, payroll, payment, and personal finance solutions for small and medium-sized businesses, individuals, and accounting professionals. Today, Intuit serves approximately 100 million customers worldwide through products like TurboTax, Credit Karma, QuickBooks, and Mailchimp, empowering individuals and small businesses to optimize their financial efficiency and make confident financial decisions.

To effectively manage the breadth and depth of customer data across its products and implement robust security measures, Intuit must maintain a clear understanding of its infrastructure. As Zach Probst, a software engineer at Intuit, stated: “Intuit’s brand is built on being a reliable company and a responsible steward of customer data, which means we need to respond swiftly to security incidents.”

Security Challenges
Security is paramount, so engineers must be prepared to quickly identify and patch vulnerabilities in Intuit’s extensive technology stack to prevent exploitation and protect customers from risks.

“Intuit is a large company with significant influence. Mapping so much computing and network infrastructure is already a massive challenge,” Probst explained. “But we also need to consider attribution, prioritization, and hygiene. Understanding who owns which endpoints, which vulnerabilities are most critical, and which infrastructure is no longer in use can significantly raise the stakes.”

Addressing security vulnerabilities requires deep insight into the affected software, operating systems, or environments. Intuit found it challenging to perform endpoint-to-asset attribution—the process of linking individual hostnames within a domain to their respective assets. This is crucial for achieving comprehensive visibility in security responses and ensuring sensitive information remains secure. The process was also time-consuming and relied on complex manual testing requiring specialized expertise.

The Solution
Intuit overcame this challenge by using Neo4j’s knowledge graph to map its network of over 500,000 endpoints, enabling precise network topology mapping to locate security vulnerabilities.

Probst explained: “This setup allows more people to understand network interdependencies, ensuring vulnerabilities are resolved quickly and customer data remains secure.” Intuit needed to stay ahead.

Powerful and Immediate Incident Response
Intuit’s Security Knowledge and Insights Platform (SKIP) leverages a knowledge graph integrating interconnected datasets, including vulnerabilities from security scans, cloud resources, compliance frameworks, organizational charts, DNS zones, entries, source code repositories and contributors, Akamai property configurations, redirect rules, and other sources.

The knowledge graph is refreshed with new data using Nodestream, an open-source ETL (extract, transform, load) framework for graph databases developed by Intuit’s team. Nodestream also supports data ingestion from sources like Kafka, AWS Athena, flat files, and Akamai. Neo4j Bloom is then used for simple graph visualization and exploration.

Before Neo4j, the team couldn’t map how Intuit’s infrastructure operated through Akamai, a distributed platform for cloud computing, security, and content delivery. Akamai includes hundreds of property configurations, each with thousands of lines of settings and thousands of endpoints, making traffic routing difficult and time-consuming.

Probst said: “Using graph relationships allows us to make powerful inferences, unlocking information that would otherwise remain hidden in siloed data. Nothing beats Bloom out of the box. It works seamlessly with minimal fuss, which was incredibly convenient when we started this project.”

With Neo4j and Bloom, Intuit now has a clear understanding of how its data and network traffic are routed through Akamai. Chad Cloes, a senior staff software engineer at Intuit, noted: “These visualizations reveal previously hidden insights that were hard to see before, such as identifying where unused infrastructure might be lurking.”

The team can now link Common Vulnerabilities and Exposures (CVEs) to source code and connect that code to frontend endpoints, mapping potential exposures in unused or unmonitored infrastructure. Probst pointed out: “This allows us to address the most critical vulnerabilities first and allocate resources appropriately to handle them.”

Cloes agreed: “We can now map potential exposures in seconds—something that previously took engineers hours or even days to figure out manually.”

The Fastest Path to a More Secure Infrastructure: Data Returns in Milliseconds
Given its massive data and network footprint, Probst emphasized: “It’s critical that we can attribute over 500,000 endpoints to hostnames in milliseconds, simply because we can add new data so quickly. We can pivot on zero-day vulnerabilities, assess our exposure, and resolve them almost immediately.” This is as close to real-time response as one could hope for.

Intuit now achieves immense throughput, ingesting and linking 20 million events and performing 75 million database updates per hour in the graph, which contains 65 million nodes and 190 million relationships.

Ultimately, more thorough infrastructure mapping leads to a more secure environment with significantly lower risks of security incidents. Another benefit of a clearer system view: time savings and a dramatic boost in developer productivity.

“Thanks to Neo4j, our developer team can calculate risk scores for every asset in Intuit—defined as any software, server, service, website, source code, etc.—in just four minutes. These are complex traversals involving tens of thousands of assets, and we can complete them incredibly quickly,” said Zach Probst, Staff Software Engineer at Intuit.

The Neo4j graph database platform helps businesses deeply, easily, and quickly uncover hidden relationships and patterns across billions of data connections. Customers leverage the structure of their connected data to reveal innovative solutions to their most pressing business challenges, from fraud detection and customer 360 to knowledge graphs, supply chains, personalization, IoT, and network management.

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