Skip to content
Database ⇄ Data warehouse

AWS Aurora PostgreSQL to MotherDuck integration — real-time, two-way sync

Keep AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL and MotherDuck

Connect AWS Aurora PostgreSQL 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.

Teams connect AWS Aurora PostgreSQL to MotherDuck to run analytics off the operational database. Aurora tables and rows replicate into MotherDuck schemas, keeping analytical queries and Database Shares off the transactional workload.

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

Common use cases

  • 01 Offload heavy analytical queries from Aurora views and materialized views to MotherDuck.
  • 02 Give analysts current production data via MotherDuck schemas without granting Aurora access.
  • 03 Keep a warehouse copy of Aurora tables current without batch export jobs.
  • 04 Share curated, synced datasets with other teams through read-only database shares

Common sync patterns

Operational-to-analytical replication

Aurora PostgreSQL tables and rows sync continuously into MotherDuck tables for analytics.

Shared analytics layer

replicated data is exposed through MotherDuck Database Shares so other teams query it without touching Aurora.

Local-plus-cloud analysis

synced tables combine with attached local DuckDB databases for ad hoc analysis.

What you can sync between AWS Aurora PostgreSQL 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.

AWS Aurora PostgreSQL objects MotherDuck objects How this pairing syncs
Tables The core sync unit; rows are matched across systems by primary key. 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.
Primary keys and constraints Identify rows for upserts and enforce integrity on sync writes. Databases Cloud-hosted DuckDB databases that scope a sync's reads and writes. Primary keys and constraints is specific to AWS Aurora PostgreSQL and Databases to MotherDuck — each maps to any object or custom field on the other side.
Views and materialized views Usable as read-only sources for filtered or precomputed sync datasets. Schemas Namespaces within a database used to organize synced tables. Views and materialized views is specific to AWS Aurora PostgreSQL and Schemas to MotherDuck — each maps to any object or custom field on the other side.
Foreign keys Relationship metadata that syncs can translate into object references elsewhere. Views Modeled projections used as outbound sync sources. Foreign keys is specific to AWS Aurora PostgreSQL and Views to MotherDuck — each maps to any object or custom field on the other side.
Replication slots and publications The logical replication objects that power log-based CDC. Database Shares Read-only copies of a database shared with other users or teams. Replication slots and publications is specific to AWS Aurora PostgreSQL and Database Shares to MotherDuck — each maps to any object or custom field on the other side.
Databases and schemas PostgreSQL's two-level namespace scopes which tables a sync connection targets. Attached Local DuckDB Databases Local files attached alongside cloud databases for hybrid queries. Databases and schemas is specific to AWS Aurora PostgreSQL and Attached Local DuckDB Databases to MotherDuck — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora PostgreSQL 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.

AWS Aurora PostgreSQL MotherDuck Sub-second propagation

DetectionChanges in AWS Aurora PostgreSQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback.

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

MotherDuck AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL ⇄ MotherDuck

Connect AWS Aurora PostgreSQL and MotherDuck for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL or MotherDuck record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between AWS Aurora PostgreSQL and MotherDuck.

How the AWS Aurora PostgreSQL and MotherDuck connectors work

AWS Aurora PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback
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 AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL 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
    AWS Aurora PostgreSQL connected
    MotherDuck connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

AWS Aurora PostgreSQL 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 395 integrations available for AWS Aurora PostgreSQL and MotherDuck.

Popular · 5 of 395
All AWS Aurora PostgreSQL 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