Graph Intelligence On Databricks. Zero Data Egress.

Query multi-hop relationships directly on your Delta Lake tables. No ETL pipelines. No data copies. No egress fees. Your data never leaves Databricks.

Book a Demo
10

mins

data to graph

5

Secs

5-hop query

10

pb

in production

$0

Data Egress

You Have the Data. You Just Can't Traverse It.

External Graph DBs Mean Egress

External graph databases require copying your data out of Databricks. That means egress fees, stale data, sync pipelines, and a second security perimeter.

Spark Wasn't Built for Graphs

Each hop requires a full distributed query. A 5-hop traversal takes 30-60 seconds on Spark — and costs a running cluster the entire time.

JOINs Don't Scale for Relationships

Self-joining a 200M-row table 5 times produces billions of intermediate rows. SQL was designed for set operations, not path traversal.

External Graph DBs Mean Egress

External graph databases require copying your data out of Databricks. That means egress fees, stale data, sync pipelines, and a second security perimeter.

Spark Wasn't Built for Graphs

Each hop requires a full distributed query. A 5-hop traversal takes 30-60 seconds on Spark — and costs a running cluster the entire time.

JOINs Don't Scale for Relationships

Self-joining a 200M-row table 5 times produces billions of intermediate rows. SQL was designed for set operations, not path traversal.

External Graph DBs Mean Egress

External graph databases require copying your data out of Databricks. That means egress fees, stale data, sync pipelines, and a second security perimeter.

Spark Wasn't Built for Graphs

Each hop requires a full distributed query. A 5-hop traversal takes 30-60 seconds on Spark — and costs a running cluster the entire time.

JOINs Don't Scale for Relationships

Self-joining a 200M-row table 5 times produces billions of intermediate rows. SQL was designed for set operations, not path traversal.

External Graph DBs Mean Egress

External graph databases require copying your data out of Databricks. That means egress fees, stale data, sync pipelines, and a second security perimeter.

Spark Wasn't Built for Graphs

Each hop requires a full distributed query. A 5-hop traversal takes 30-60 seconds on Spark — and costs a running cluster the entire time.

JOINs Don't Scale for Relationships

Self-joining a 200M-row table 5 times produces billions of intermediate rows. SQL was designed for set operations, not path traversal.

Your Data Already Has Relationships. LakeGraph Makes Them Queryable — Without Moving a Single Byte.

Model
Point LakeGraph at your Delta Lake tables. AI discovers entities, infers relationships, and builds a persistent graph index — powered by Liquid Clustering for instant lookups.
Query

Run 1-to-5 hop traversals in seconds via Lakebase — with pre-computed adjacency lists and three-layer caching. No Spark cluster cold starts.
Operate

Feed graph features directly into Databricks ML pipelines, BI dashboards, and real-time applications — all governed by Unity Catalog.

From Tables To Traversals,
In Minutes

Book a Demo

1.
Connect To Your Lakehouse

Point LakeGraph at your Databricks Unity Catalog tables. No data copying — LakeGraph reads governed Delta Lake tables in place, respecting your existing access controls and lineage.

2.
Define Your Graph Schema

Tell the AI what you want to explore, or manually declare how entities connect. LakeGraph builds a persistent graph index using Liquid Clustering — optimized for traversal, not full scans.

3.
Query Relationships

Run multi-hop traversals through a fast interactive query layer. 1-hop in under 5ms, 5-hop in seconds. Results stay inside your Databricks workspace — no egress, no external databases.

4.
Operationalize & Monitor

Feed graph insights into ML pipelines, investigations, and dashboards. LakeGraph syncs automatically as your Delta tables change — keeping your graph fresh without manual rebuilds.

1. Connect To Your Lakehouse

Point LakeGraph at your Databricks Unity Catalog tables. No data copying — LakeGraph reads governed Delta Lake tables in place, respecting your existing access controls and lineage.

2.
Define Your Graph Schema

Tell the AI what you want to explore, or manually declare how entities connect. LakeGraph builds a persistent graph index using Liquid Clustering — optimized for traversal, not full scans.

3.
Query Relationships

Run multi-hop traversals through a fast interactive query layer. 1-hop in under 5ms, 5-hop in seconds. Results stay inside your Databricks workspace — no egress, no external databases.

4.
Operationalize & Monitor

Feed graph insights into ML pipelines, investigations, and dashboards. LakeGraph syncs automatically as your Delta tables change — keeping your graph fresh without manual rebuilds.

1.
Connect To Your Lakehouse

Point LakeGraph at your Databricks Unity Catalog tables. No data copying — LakeGraph reads governed Delta Lake tables in place, respecting your existing access controls and lineage.

2.
Define Your Graph Schema

Tell the AI what you want to explore, or manually declare how entities connect. LakeGraph builds a persistent graph index using Liquid Clustering — optimized for traversal, not full scans.

3.
Query Relationships

Run multi-hop traversals through a fast interactive query layer. 1-hop in under 5ms, 5-hop in seconds. Results stay inside your Databricks workspace — no egress, no external databases.

4.
Operationalize & Monitor

Run multi-hop traversals through a fast interactive query layer. 1-hop in under 5ms, 5-hop in seconds. Results stay inside your Databricks workspace — no egress, no external databases.

From Tables To Traversals,
In Minutes

Get Started

1.
Connect To Your Lakehouse

Point LakeGraph at your Databricks Unity Catalog tables. No data copying — LakeGraph reads governed Delta Lake tables in place, respecting your existing access controls and lineage.

Connect Databricks or Snowflake tables.

LakeGraph reads governed data in place.

2.
Define Your Graph Schema

Tell the AI what you want to explore, or manually declare how entities connect. LakeGraph builds a persistent graph index using Liquid Clustering — optimized for traversal, not full scans.

Define how rows connect — users, devices, transactions, events.

A persistent graph index is created automatically.

3.
Query Relationships

Run multi-hop traversals through a fast interactive query layer. 1-hop in under 5ms, 5-hop in seconds. Results stay inside your Databricks workspace — no egress, no external databases.

Run traversals for investigations, analytics, and
ML in the same environment.

4.
Operationalize & Monitor

Feed graph insights into ML pipelines, investigations, and dashboards. LakeGraph syncs automatically as your Delta tables change — keeping your graph fresh without manual rebuilds.

Define how rows connect — users, devices, transactions, events.

A persistent graph index is created automatically.

1. Connect To Your Lakehouse

Point LakeGraph at your Databricks Unity Catalog tables. No data copying — LakeGraph reads governed Delta Lake tables in place, respecting your existing access controls and lineage.

2.
Define Your Graph Schema

Tell the AI what you want to explore, or manually declare how entities connect. LakeGraph builds a persistent graph index using Liquid Clustering — optimized for traversal, not full scans.

3.
Query Relationships

Run multi-hop traversals through a fast interactive query layer. 1-hop in under 5ms, 5-hop in seconds. Results stay inside your Databricks workspace — no egress, no external databases.

4.
Operationalize & Monitor

Feed graph insights into ML pipelines, investigations, and dashboards. LakeGraph syncs automatically as your Delta tables change — keeping your graph fresh without manual rebuilds.

Problems You Can’t Solve

With Tables Alone

Finance

Detect fraud rings and money mule networks across accounts, devices, and transactions — in seconds, not hours. No data leaves your Databricks workspace, keeping you audit-ready.

Manufacturing

Trace components across suppliers, production lines, and logistics partners. Surface counterfeit parts, single-source risks, and quality failure chains before they reach customers.

Healthcare

Connect patients, providers, claims, and referral patterns to detect billing anomalies, care gaps, and provider network risks — all within your HIPAA-compliant Databricks environment.

Commercial Real Estate

Map ownership structures, investment flows, and tenant relationships across properties, funds, and submarkets. Surface hidden concentration risk and beneficial ownership chains.

Designed For

Databricks-Native Architectures

Works with governed datasets. Aligns with existing access controls.
Fits standard data engineering workflows.

Book a Demo

LakeGraph vs. External Graph Databases — Why Your Data Should Stay Where It Is

Capability
External Graph DB
Query relationships on Databricks data without copying it to another system
Reuse Unity Catalog governance (access control + lineage)
No ETL or sync pipelines just to keep a separate graph copy up to date
No separate graph database infrastructure to operate and secure
Ship results in formats teams already use (graph outputs + tabular projections)
Zero data egress — no bytes leave Databricks
No additional infrastructure to provision or manage
AI-powered schema discovery and relationship inference
Pay only for Databricks compute you already use
Automatic graph refresh via Delta Lake Change Data Feed

Databricks-first

Built on Databricks, Not Bolted On

Delta Lake + Liquid Clustering
Your graph lives as Delta tables with Liquid Clustering. No full Parquet scans — data is physically organized by node and edge IDs so the engine reads only the files it needs.
Lakebase Query Engine
Interactive queries run on Lakebase (Databricks-managed Postgres), not Spark. No cold starts. Pre-computed adjacency lists deliver 1-hop lookups in under 5 milliseconds.
Three-Layer Caching
Automatic buffer cache, pre-built neighbor arrays, and a smart hop cache work together so repeated and deep traversals return in seconds, not minutes.

Built for Compliance. Built for Databricks.

LakeGraph runs entirely inside your Databricks workspace. Your data never leaves. Every query is governed by Unity Catalog — the same access controls, audit logs, and lineage tracking your compliance team already trusts. No shadow infrastructure. No egress. No new attack surface.

Zero data egress — your graph data never leaves the Databricks perimeter

Unity Catalog enforced — row-level security, column masking, and full lineage on every graph query

SOC 2 and HIPAA-ready — no additional compliance certification needed beyond your existing Databricks deployment