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Data warehouse ⇄ Database

Databricks to Neo4j integration — real-time, two-way sync

Keep Databricks and Neo4j in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

  • SOC 2 and 6 other compliance frameworks
  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Databricks and Neo4j

Connect Neo4j and Databricks with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.

Operational databases and analytical warehouses want the same data at different moments. Analysts want Neo4j's rows in Databricks, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in Neo4j where the services that read from it get them at normal query latency.

Stacksync covers both directions with one connection. Tables or collections in Neo4j sync into Databricks in real time, and result tables in Databricks sync back into Neo4j, with schema and type mapping between the two systems handled for you.

Common use cases

  • Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
  • Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • Write computed relationship scores (fraud, influence, similarity) back to operational systems.
  • Keep a customer-360 graph continuously updated from ERP, CRM, and support sources.

Fresh analytics without loading windows

Because changes stream continuously, analysts query current data instead of waiting for last night's load.

Offload heavy reads

Point analytical queries at the synced copy in Databricks and keep Neo4j focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Neo4j land in Databricks as they change, replacing hand-built CDC and batch extract jobs.

What you can sync between Databricks and Neo4j

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Databricks objects Neo4j objects
Volumes Unity Catalog file storage used for staging bulk loads. Indexes & Constraints Uniqueness constraints and indexes that make MERGE-based upserts reliable and fast.
SQL Warehouses The compute endpoint a sync connects to for query execution. Databases Named databases in a single instance that scope multi-tenant or multi-domain syncs.
Change Data Feed Row-level change records on Delta tables that drive incremental reads. Users & Roles Security principals controlling what an integration credential can query or modify.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Nodes Entity records (customers, products, accounts) written from source systems as labeled nodes.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. Relationships Typed, directed edges that carry the connections syncs exist to model.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. Properties Key-value attributes on both nodes and relationships, mapped from source fields.
What ships with Databricks ⇄ Neo4j

Connect Databricks and Neo4j for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Neo4j connection.

Real-time

Two-way sync

Changes in Databricks or Neo4j instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Neo4j data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Databricks or Neo4j record.

Observability

Monitoring

Track your Databricks ⇄ Neo4j sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Databricks and Neo4j.

How the Databricks and Neo4j connectors work

Databricks

Integration surface
SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
Authentication
Personal access tokens or OAuth machine-to-machine credentials for service principals
Change detection
Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
Capabilities
read · write · CDC
Rate limits
Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits

Neo4j

Integration surface
Bolt binary protocol with Cypher via official drivers, plus an HTTP query API
Authentication
Username/password (basic auth); enterprise deployments add SSO options
Change detection
Neo4j Change Data Capture on Enterprise and Aura streams graph changes; otherwise Cypher polling on timestamp properties
Capabilities
read · write · CDC
How it works

How to connect Databricks to Neo4j — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Databricks and Neo4j with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Databricks connected
    Neo4j connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Databricks and Neo4j objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Databricks ⇄ Neo4j
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Databricks Neo4j
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Databricks and Neo4j integration FAQ

SECURITY

Security teams love Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

Learn more about security
SOC 2 type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 386 integrations available for Databricks and Neo4j.

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