The Problem With Relational Thinking

Every enterprise runs on relationships. Your suppliers have sub-suppliers. Your customers are connected to other customers through shared entities. Fraudulent transactions leave trails that only become visible when you trace the network.

But most data infrastructure is built for tables, not graphs. You can count. You can aggregate. You can join. What you can't do easily is ask: who is connected to whom, through how many hops, and what does that network reveal?

The answer has historically been: buy a graph database. Extract data from your lakehouse. Load it into Neo4j or a similar platform. Build a separate query layer. Hope the data stays in sync.

That architecture is expensive, slow to maintain, and creates exactly the kind of data silo it was meant to solve.

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LakeGraph on Databricks Lakebase is a different approach entirely.

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Graph Analytics — Without Leaving the Lakehouse

LakeGraph is a Databricks-native graph analytics platform implemented by Frisco Analytics. It turns your existing operational data — Delta Tables, CSVs, PDFs, contracts — into a property graph, and surfaces decision-grade insights without ever moving data outside Databricks.

Delta Lake holds the source of truth. Lakebase serves the graph as Postgres tables with sub-5ms 1-hop lookups and sub-second multi-hop traversals. Unity Catalog governs every node, every edge, every query — end to end.

No separate graph database. No ETL pipeline to maintain. No synchronization lag between your lakehouse and your graph layer. The graph lives where your data already lives.

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What Customers Are Building

Vendor Risk — procurement and supply chain

Most companies think they know their supplier base. But the real risk isn't your direct suppliers — it's the sub-suppliers they depend on that you've never seen.

LakeGraph maps your entire supplier network as a property graph. It identifies concentration risk — vendors who supply multiple critical components, or sub-suppliers shared across your top 10 vendors — before it becomes a disruption. Run PageRank across your supplier network and the most structurally critical nodes surface immediately. What used to take weeks of manual analysis runs in seconds.

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Fraud Paths — financial services and insurance

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Fraud rarely happens in isolation. A fraudulent claim, a suspicious transaction, a bad actor — they leave traces in the relationships between entities that standard SQL queries are blind to.

LakeGraph's agentic orchestrator runs community detection, betweenness centrality, and flow algorithms against your existing Delta data. Relationships that look innocent in isolation become visible as coordinated patterns when you see the graph. Claims that share addresses, phone numbers, or intermediaries — entities connected through two or three hops of separation — emerge as clusters your fraud team can investigate.

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Supply Chain Network Analysis — manufacturing and distribution

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A supply chain disruption is never just about one node going down. It's about how that failure propagates — which downstream plants are affected, which orders are at risk, which customers will feel it first.

LakeGraph models your supply chain as a connected network. When a supplier goes offline, you can immediately traverse the graph to understand second and third-order exposure — not after the fact, but in real time, as the situation develops.

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Network Analysis — telecommunications, utilities, financial services

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Understanding how your network behaves under stress — which nodes are bottlenecks, which connections carry disproportionate load, where failure would cascade — requires graph thinking. LakeGraph brings PageRank, betweenness centrality, and community algorithms to your existing operational data, queryable by any analyst in plain English through the Ask AI interface.

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The Architecture Under the Hood

Delta Lake — Source of Truth NodeStore, RelationshipStore, and NodeIndex all live in Delta Lake — Liquid Clustered, CDF-enabled. Your graph data benefits from the same versioning, time travel, and governance as the rest of your lakehouse.

Lakebase — Postgres Serving Layer The graph is served through four Lakebase tables: lg_nodes (node_id PK), lg_edges (src · dst), lg_adjacency (pre-computed), and lg_hop_cache (TTL cache). Three-layer caching — shared_buffers, adjacency cache, hop_cache — delivers sub-5ms 1-hop neighbor lookups and sub-second multi-hop traversals. Repeat queries are near-instant.

LakeGraph Application Layer The application layer handles agentic orchestration, query routing, and algorithm tool calls — PageRank, betweenness, communities, flows — against the Lakebase-served graph. Business users interact through Ask AI, Analytics Chat, dashboards, or REST/SQL API. No graph query language to learn.

LLM Build Agents on Serverless SQL Warehouse Schema inference, entity resolution, property enrichment, and relationship inference run as LLM-powered agents on Databricks Serverless. Your raw operational data — including unstructured sources like PDFs and contracts — gets resolved into graph-ready entities automatically.

Unity Catalog — Governance End to End Access controls, data lineage, audit logs, and governance apply across both Delta Lake and Lakebase. Every node and edge in the graph is subject to the same Unity Catalog policies as the rest of your data estate.

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Zero data egress. The entire graph lifecycle — build, enrich, serve, query — runs inside Databricks.

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Getting Started

Every engagement starts with a half-day discovery workshop. We map your existing data assets, identify the relationship patterns with the highest business value — vendor risk, fraud detection, network analysis — and scope a path to a production graph on Databricks.

Most teams are surprised how much relationship intelligence is already sitting in their Delta Tables, waiting to be unlocked.

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Frisco Analytics is a certified LakeFusion SI partner and Databricks Brickbuilder partner, implementing LakeGraph on the Databricks Lakehouse.

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Contact: contact@friscoanalytics.com 

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