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

Keep AWS Aurora MySQL and PostgreSQL in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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  • 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 AWS Aurora MySQL and PostgreSQL

Keep AWS Aurora MySQL and PostgreSQL synchronized in real time, across engines, regions, or services, in one or both directions.

Syncing AWS Aurora MySQL with PostgreSQL keeps two relational engines aligned at the Table and Row level, typically during a migration, for cross-engine replication, or to serve teams standardized on different databases. Schemas, Columns, and Primary and Unique Keys map between the systems so both sides stay queryable.

Stacksync syncs tables or collections between AWS Aurora MySQL and PostgreSQL continuously and bi-directionally, translating types between the two engines and resolving conflicts by rules you configure. Rows written on either side appear on the other within seconds.

Common use cases

  • 01 Migrate off Aurora MySQL to PostgreSQL without a hard cutover by running both in sync.
  • 02 Give a Postgres-standardized analytics stack live access to data produced by a MySQL application.
  • 03 Maintain consistent Primary and Unique Keys across engines so downstream joins remain reliable.
  • 04 Stream row changes from Aurora into SaaS tools via binlog CDC instead of scheduled batch exports.

Common sync patterns

Cross-engine replication

Tables and Rows in Aurora MySQL stay synchronized with the corresponding PostgreSQL Tables and Schemas.

Migration with dual-run

both databases receive writes during a MySQL-to-Postgres cutover, with Columns and keys mapped automatically.

Reporting replica

Aurora production Tables feed PostgreSQL Materialized Views used by the analytics team.

What you can sync between AWS Aurora MySQL and PostgreSQL

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 PostgreSQL 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 primary sync target; rows map one-to-one to records in connected SaaS systems. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Columns MySQL data types are mapped to the paired system's field types during schema setup. Columns Field-level mapping targets; types are mapped to the connected system's field types. 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 Read-side projections used to expose joined or filtered data to a sync. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Primary keys and indexes Used to match rows across systems and keep incremental syncs efficient. Primary and Unique Keys Used as match keys for idempotent upserts and conflict resolution. Primary keys and indexes is specific to AWS Aurora MySQL and Primary and Unique Keys to PostgreSQL — each maps to any object or custom field on the other side.
Foreign keys Express relationships that syncs preserve when mapping to related objects elsewhere. JSONB Columns Hold semi-structured payloads such as nested SaaS objects or metadata. Foreign keys is specific to AWS Aurora MySQL and JSONB Columns to PostgreSQL — each maps to any object or custom field on the other side.
Stored procedures and triggers Existing database logic keeps firing on rows written by a sync. Sequences Generate surrogate keys for rows created by inbound syncs. Stored procedures and triggers is specific to AWS Aurora MySQL and Sequences to PostgreSQL — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora MySQL and PostgreSQL

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 PostgreSQL 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 PostgreSQL as a row-level write, with types converted between the two schemas.

PostgreSQL AWS Aurora MySQL Sub-second propagation

DetectionChanges in PostgreSQL are captured at the source via change data capture — no polling loop against its API. Logical replication (wal_level = logical) for change data capture via the "Postgres" connector.

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

Rate-limit considerations

  • PostgreSQL: No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput.
What ships with AWS Aurora MySQL ⇄ PostgreSQL

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

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

Real-time

Two-way sync

Changes in AWS Aurora MySQL or PostgreSQL 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 PostgreSQL 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 PostgreSQL record.

Observability

Monitoring

Track your AWS Aurora MySQL ⇄ PostgreSQL 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 PostgreSQL.

How the AWS Aurora MySQL and PostgreSQL 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

PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL frontend/backend protocol)
Authentication
Database credentials (connection string or parameters), with optional SSL root certificate upload and optional SSH tunnel (SSH user + host); a least-privilege DB user
Change detection
Logical replication (wal_level = logical) for change data capture via the "Postgres" connector; database triggers (TRIGGER grant + stacksync_logging schema) via the trigger-based "Postgres Heroku" connector where
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput
PostgreSQL setup guide
How it works

How to connect AWS Aurora MySQL to PostgreSQL — 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 PostgreSQL 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
    PostgreSQL connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

AWS Aurora MySQL and PostgreSQL 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 489 integrations available for AWS Aurora MySQL and PostgreSQL.

Popular · 7 of 489
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