Skip to content
Data warehouse ⇄ Database

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

Keep MotherDuck 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

Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect MotherDuck and Neo4j

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

Common use cases

  • 01 Land CRM and operational database records in MotherDuck so a small team gets warehouse-style analytics without cluster management
  • 02 Sync modeled MotherDuck tables outward to operational tools for activation
  • 03 Feed identity and access data into a graph for entitlement and blast-radius analysis.
  • 04 Write computed relationship scores (fraud, influence, similarity) back to operational systems.

Common sync patterns

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 MotherDuck and keep Neo4j focused on its operational workload.

Operational data in the warehouse, minus the pipeline

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

What you can sync between MotherDuck 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.

MotherDuck objects Neo4j objects How this pairing syncs
Databases Cloud-hosted DuckDB databases that scope a sync's reads and writes. Databases Named databases in a single instance that scope multi-tenant or multi-domain syncs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Schemas Namespaces within a database used to organize synced tables. Properties Key-value attributes on both nodes and relationships, mapped from source fields. Schemas is specific to MotherDuck and Properties to Neo4j — each maps to any object or custom field on the other side.
Tables The main landing target for synced records and source for analysis. Labels Node type markers used to map source tables or objects onto the graph. Tables is specific to MotherDuck and Labels to Neo4j — each maps to any object or custom field on the other side.
Views Modeled projections used as outbound sync sources. Indexes & Constraints Uniqueness constraints and indexes that make MERGE-based upserts reliable and fast. Views is specific to MotherDuck and Indexes & Constraints to Neo4j — each maps to any object or custom field on the other side.
Database Shares Read-only copies of a database shared with other users or teams. Users & Roles Security principals controlling what an integration credential can query or modify. Database Shares is specific to MotherDuck and Users & Roles to Neo4j — each maps to any object or custom field on the other side.
Attached Local DuckDB Databases Local files attached alongside cloud databases for hybrid queries. Nodes Entity records (customers, products, accounts) written from source systems as labeled nodes. Attached Local DuckDB Databases is specific to MotherDuck and Nodes to Neo4j — each maps to any object or custom field on the other side.

How changes propagate between MotherDuck and Neo4j

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

MotherDuck Neo4j Interval-based propagation

DetectionStacksync polls MotherDuck for changes on an incremental schedule, reading only records changed since the previous pass. Polling.

DeliveryEach detected change is written to Neo4j through its API, with automatic retries and rate-limit backoff.

Neo4j MotherDuck Sub-second propagation

DetectionChanges in Neo4j are captured at the source via change data capture — no polling loop against its API. Neo4j Change Data Capture on Enterprise and Aura streams graph changes.

DeliveryEach detected change is applied to MotherDuck as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • MotherDuck: Subject to the platform's compute and concurrency limits rather than per-request API rate limits.
What ships with MotherDuck ⇄ Neo4j

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever MotherDuck 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 MotherDuck or Neo4j record.

Observability

Monitoring

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

Trading partners

EDI

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

How the MotherDuck and Neo4j connectors work

MotherDuck

Integration surface
SQL through DuckDB clients and drivers using a MotherDuck (md:) connection
Authentication
Access token created in MotherDuck (Settings > General > Create Token), pasted into Stacksync; database name and schema configurable if not using defaults
Change detection
Polling; no log-based CDC or webhook surface is exposed
Capabilities
read · write
Rate limits
Subject to the platform's compute and concurrency limits rather than per-request API rate limits
MotherDuck setup guide

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

    Choose tables

    Pick the MotherDuck 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 · MotherDuck ⇄ 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
    MotherDuck Neo4j
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

MotherDuck and Neo4j integration FAQ

SECURITY

Security teams trust 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
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 460 integrations available for MotherDuck and Neo4j.

Popular · 6 of 460
All MotherDuck integrations All Neo4j integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own