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Database ⇄ Data warehouse

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

Keep AWS Aurora MySQL 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
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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect AWS Aurora MySQL and MotherDuck

Connect AWS Aurora MySQL 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 replicate AWS Aurora MySQL into MotherDuck to run analytics off the operational database. Aurora tables and rows land in MotherDuck schemas and tables, keeping analytical queries and Database Shares off the production instance while the warehouse stays current.

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

Common use cases

  • 01 Move BI workloads off Aurora by pointing dashboards at MotherDuck tables.
  • 02 Share curated views of production data via MotherDuck Database Shares without granting Aurora access.
  • 03 Analyze Aurora rows alongside local DuckDB files during data exploration.
  • 04 Share curated, synced datasets with other teams through read-only database shares

Common sync patterns

Operational-to-warehouse replication

Aurora MySQL tables and rows continuously load into MotherDuck tables for analytical queries.

Governed sharing

replicated Aurora data is exposed to other teams through MotherDuck Database Shares instead of production credentials.

Local-plus-cloud analysis

Aurora data in MotherDuck is combined with Attached Local DuckDB Databases for ad hoc analysis.

What you can sync between AWS Aurora MySQL 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 MySQL objects MotherDuck objects How this pairing syncs
Tables The primary sync unit; each table maps one-to-one to a table or object in the paired system. 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 Can serve as read-only sync sources for derived or filtered datasets. 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.
Stored procedures and triggers Existing database logic keeps firing on rows written by a sync. Databases Cloud-hosted DuckDB databases that scope a sync's reads and writes. Stored procedures and triggers is specific to AWS Aurora MySQL and Databases to MotherDuck — each maps to any object or custom field on the other side.
Databases (schemas) Logical namespaces that scope which tables a sync connection can see. Schemas Namespaces within a database used to organize synced tables. Databases (schemas) is specific to AWS Aurora MySQL and Schemas to MotherDuck — each maps to any object or custom field on the other side.
Rows Inserted, updated, and deleted individually or in bulk during two-way syncs. Database Shares Read-only copies of a database shared with other users or teams. Rows is specific to AWS Aurora MySQL and Database Shares to MotherDuck — each maps to any object or custom field on the other side.
Columns MySQL data types are mapped to the paired system's field types during schema setup. Attached Local DuckDB Databases Local files attached alongside cloud databases for hybrid queries. Columns is specific to AWS Aurora MySQL 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 MySQL 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 MySQL MotherDuck Sub-second propagation

DetectionChanges in AWS Aurora MySQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns 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 MySQL 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 MySQL 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 MySQL ⇄ MotherDuck

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

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

Real-time

Two-way sync

Changes in AWS Aurora MySQL 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 MySQL 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 MySQL or MotherDuck record.

Observability

Monitoring

Track your AWS Aurora MySQL ⇄ 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 MySQL and MotherDuck.

How the AWS Aurora MySQL and MotherDuck connectors work

AWS Aurora MySQL

Integration surface
SQL wire protocol (MySQL-compatible), standard MySQL drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns 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 MySQL 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 MySQL 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 MySQL connected
    MotherDuck connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

AWS Aurora MySQL 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 MySQL and MotherDuck.

Popular · 4 of 395
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