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Data warehouse ⇄ Database

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

Keep Materialize 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 Materialize and Neo4j

Connect Neo4j and Materialize 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 Materialize, 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 Materialize in real time, and result tables in Materialize sync back into Neo4j, with schema and type mapping between the two systems handled for you.

Common use cases

  • Drive alerting and operational tooling from SUBSCRIBE change streams instead of scheduled queries.
  • Maintain real-time inventory, usage, or account-health counters consumed by customer-facing applications.
  • Sync product catalog and order history into Neo4j to power recommendation queries.
  • Feed identity and access data into a graph for entitlement and blast-radius analysis.

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 Materialize and keep Neo4j focused on its operational workload.

Operational data in the warehouse, minus the pipeline

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

What you can sync between Materialize 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.

Materialize objects Neo4j objects
Sinks Outbound connections that emit view changes to Kafka topics. Indexes & Constraints Uniqueness constraints and indexes that make MERGE-based upserts reliable and fast.
Indexes In-memory arrangements that make view reads fast for serving workloads. Databases Named databases in a single instance that scope multi-tenant or multi-domain syncs.
Clusters Compute pools that isolate ingestion, view maintenance, and serving. Users & Roles Security principals controlling what an integration credential can query or modify.
Connections & Secrets Stored credentials and endpoints used by sources and sinks. Nodes Entity records (customers, products, accounts) written from source systems as labeled nodes.
Schemas & Databases Namespaces that organize objects a sync targets. Relationships Typed, directed edges that carry the connections syncs exist to model.
Tables User-managed tables that accept INSERT/UPDATE/DELETE from sync pipelines. Properties Key-value attributes on both nodes and relationships, mapped from source fields.
What ships with Materialize ⇄ Neo4j

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Materialize 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 Materialize or Neo4j record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Materialize and Neo4j connectors work

Materialize

Integration surface
PostgreSQL wire protocol (SQL)
Authentication
Database credentials (username/password; app passwords in the managed cloud service)
Change detection
SUBSCRIBE queries stream row-level changes of any view or table to the client
Capabilities
read · write · CDC

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

    Choose tables

    Pick the Materialize 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 · Materialize ⇄ 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
    Materialize Neo4j
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Materialize 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 Materialize and Neo4j.

Popular · 8 of 386
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