Skip to content
Data warehouse

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

Keep Materialize and MotherDuck 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 Materialize and MotherDuck

Keep tables consistent across Materialize and MotherDuck, for a migration, a multi-warehouse stack, or a dataset two platforms both need.

Companies end up with two warehouses for practical reasons: a migration in progress, teams that standardized on different platforms, an acquisition, or tools that only connect to one of them. The result is the same dataset maintained twice, with duplicated pipelines and numbers that almost match.

Stacksync syncs tables between Materialize and MotherDuck continuously, in either or both directions. Rows changed on one platform appear on the other within seconds, with schema and type mapping handled, so both warehouses answer questions with the same data.

Common use cases

  • 01 Bridge streaming sources and non-streaming destinations by materializing joins across both.
  • 02 Sync operational CRM or ERP data into Materialize so real-time views stay current without batch loads.
  • 03 Share curated, synced datasets with other teams through read-only database shares
  • 04 Land CRM and operational database records in MotherDuck so a small team gets warehouse-style analytics without cluster management

Common sync patterns

Serve tools that only connect to one platform

Mirror the datasets a BI tool, notebook, or application needs onto the platform it can actually reach.

Shared datasets across teams

Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.

Consolidation after M&A

Bring the acquired company's warehouse data across continuously instead of through one-off dumps.

What you can sync between Materialize and MotherDuck

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 MotherDuck objects How this pairing syncs
Tables User-managed tables that accept INSERT/UPDATE/DELETE from sync pipelines. Tables The main landing target for synced records and source for analysis. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Sources Ingestion points (Kafka, Postgres CDC, MySQL CDC, webhook) that feed external data into Materialize. Views Modeled projections used as outbound sync sources. Sources is specific to Materialize and Views to MotherDuck — each maps to any object or custom field on the other side.
Materialized Views Incrementally maintained query results that syncs read as continuously up-to-date datasets. Database Shares Read-only copies of a database shared with other users or teams. Materialized Views is specific to Materialize and Database Shares to MotherDuck — each maps to any object or custom field on the other side.
Sinks Outbound connections that emit view changes to Kafka topics. Attached Local DuckDB Databases Local files attached alongside cloud databases for hybrid queries. Sinks is specific to Materialize and Attached Local DuckDB Databases to MotherDuck — each maps to any object or custom field on the other side.
Indexes In-memory arrangements that make view reads fast for serving workloads. Databases Cloud-hosted DuckDB databases that scope a sync's reads and writes. Indexes is specific to Materialize and Databases to MotherDuck — each maps to any object or custom field on the other side.
Clusters Compute pools that isolate ingestion, view maintenance, and serving. Schemas Namespaces within a database used to organize synced tables. Clusters is specific to Materialize and Schemas to MotherDuck — each maps to any object or custom field on the other side.

How changes propagate between Materialize and MotherDuck

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.

Materialize MotherDuck Sub-second propagation

DetectionChanges in Materialize are captured at the source via change data capture — no polling loop against its API. SUBSCRIBE queries stream row-level changes of any view or table to the client.

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

MotherDuck Materialize 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 applied to Materialize 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 Materialize ⇄ MotherDuck

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Materialize or MotherDuck 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 MotherDuck record.

Observability

Monitoring

Track your Materialize ⇄ MotherDuck 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 MotherDuck.

How the Materialize and MotherDuck 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

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
How it works

How to connect Materialize to MotherDuck — 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 MotherDuck 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
    MotherDuck connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Materialize and MotherDuck 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 382 integrations available for Materialize and MotherDuck.

Popular · 2 of 382
All Materialize integrations All MotherDuck 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