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Database ⇄ Data warehouse

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

Keep Amazon Aurora 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

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Why teams connect Amazon Aurora and MotherDuck

Connect Amazon Aurora 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 Amazon Aurora'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 Amazon Aurora where the services that read from it get them at normal query latency.

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

Common use cases

  • 01 Sync modeled MotherDuck tables outward to operational tools for activation
  • 02 Share curated, synced datasets with other teams through read-only database shares
  • 03 Write enriched or scored records from analytics pipelines back into the Aurora tables that power an application.
  • 04 Offload sync reads to Aurora reader endpoints to avoid load on the writer instance.

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 Amazon Aurora focused on its operational workload.

Operational data in the warehouse, minus the pipeline

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

What you can sync between Amazon Aurora 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.

Amazon Aurora objects MotherDuck objects How this pairing syncs
Databases Logical databases within a cluster that scope a sync connection. Databases Cloud-hosted DuckDB databases that scope a sync's reads and writes. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Schemas Namespaces (PostgreSQL) or database-level grouping (MySQL) used in table selection. Schemas Namespaces within a database used to organize synced tables. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Tables Relational tables synced bi-directionally at row level. 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.
Views Read-only query-backed sources for downstream syncs. Views Modeled projections used as outbound sync sources. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Columns and Data Types Standard MySQL or PostgreSQL types mapped during field mapping. Database Shares Read-only copies of a database shared with other users or teams. Columns and Data Types is specific to Amazon Aurora and Database Shares to MotherDuck — each maps to any object or custom field on the other side.
Primary and Foreign Keys Constraints used to identify records and preserve relational integrity in syncs. Attached Local DuckDB Databases Local files attached alongside cloud databases for hybrid queries. Primary and Foreign Keys is specific to Amazon Aurora and Attached Local DuckDB Databases to MotherDuck — each maps to any object or custom field on the other side.

How changes propagate between Amazon Aurora 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.

Amazon Aurora MotherDuck Sub-second propagation

DetectionChanges in Amazon Aurora are captured at the source via change data capture — no polling loop against its API. Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters.

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

MotherDuck Amazon Aurora 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 Amazon Aurora as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Amazon Aurora: No API rate limits for wire-protocol access; throughput is bounded by instance class and connection limits.
  • MotherDuck: Subject to the platform's compute and concurrency limits rather than per-request API rate limits.
What ships with Amazon Aurora ⇄ MotherDuck

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Aurora 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 Amazon Aurora or MotherDuck record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Amazon Aurora and MotherDuck connectors work

Amazon Aurora

Integration surface
MySQL or PostgreSQL wire protocol (SQL); optional RDS Data API over HTTPS
Authentication
Database credentials or IAM database authentication
Change detection
Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters; polling as a fallback
Capabilities
read · write · CDC
Rate limits
No API rate limits for wire-protocol access; throughput is bounded by instance class and connection limits

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

    Choose tables

    Pick the Amazon Aurora 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 · Amazon Aurora ⇄ 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
    Amazon Aurora MotherDuck
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon Aurora 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 379 integrations available for Amazon Aurora and MotherDuck.

Popular · 2 of 379
All Amazon Aurora integrations All MotherDuck integrations Browse all integrations
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