Real-time
Two-way sync
Changes in AWS Aurora MySQL or AWS Aurora PostgreSQL instantly reflect in both systems. No stale data, no manual imports.
Database
AWS Aurora MySQL to AWS Aurora PostgreSQL integration — real-time, two-way sync
Keep AWS Aurora MySQL and AWS Aurora 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.
- 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 MySQL and AWS Aurora PostgreSQL
Keep AWS Aurora MySQL and AWS Aurora PostgreSQL synchronized in real time, across engines, regions, or services, in one or both directions.
Teams sync AWS Aurora MySQL with AWS Aurora PostgreSQL when workloads span both engines — a migration in progress, or services standardized on different databases. Tables and Rows replicate across engines with Columns and Primary keys mapped, without building custom CDC pipelines.
Stacksync syncs tables or collections between AWS Aurora MySQL and AWS Aurora 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 from Aurora MySQL to Aurora PostgreSQL gradually while applications stay live.
- 02 Serve read workloads from PostgreSQL Views and materialized views over data written in MySQL.
- 03 Keep Primary keys and constraints consistent when the same entities exist in both engines.
- 04 Stream row changes from Aurora into SaaS tools via binlog CDC instead of scheduled batch exports.
Common sync patterns
Cross-engine replication
Aurora MySQL Tables and Rows sync into Aurora PostgreSQL Tables continuously in both directions.
Migration without a hard cutover
Aurora MySQL Databases replicate into Aurora PostgreSQL schemas while both stay writable.
Shared reference data
selected Tables sync between engines so services on either side read the same Rows.
What you can sync between AWS Aurora MySQL and AWS Aurora 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 | AWS Aurora 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 core sync unit; rows are matched across systems by primary key. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Rows Inserted, updated, and deleted individually or in bulk during two-way syncs. | Rows Inserted, updated, and deleted in both directions during bi-directional syncs. | 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 Rich Postgres types including JSONB and arrays are mapped to the paired system's fields. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Foreign keys Express relationships that syncs preserve when mapping to related objects elsewhere. | Foreign keys Relationship metadata that syncs can translate into object references elsewhere. | 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. | Replication slots and publications The logical replication objects that power log-based CDC. | Views is specific to AWS Aurora MySQL and Replication slots and publications to AWS Aurora 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. | Databases and schemas PostgreSQL's two-level namespace scopes which tables a sync connection targets. | Stored procedures and triggers is specific to AWS Aurora MySQL and Databases and schemas to AWS Aurora PostgreSQL — each maps to any object or custom field on the other side. |
How changes propagate between AWS Aurora MySQL and AWS Aurora 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 AWS Aurora 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 AWS Aurora PostgreSQL as a row-level write, with types converted between the two schemas.
AWS Aurora PostgreSQL AWS Aurora MySQL 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 AWS Aurora MySQL as a row-level write, with types converted between the two schemas.
What ships with AWS Aurora MySQL ⇄ AWS Aurora PostgreSQL
Connect AWS Aurora MySQL and AWS Aurora PostgreSQL for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every AWS Aurora MySQL–AWS Aurora PostgreSQL connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever AWS Aurora MySQL or AWS Aurora 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 AWS Aurora PostgreSQL record.
Observability
Monitoring
Track your AWS Aurora MySQL ⇄ AWS Aurora 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 AWS Aurora PostgreSQL.
How the AWS Aurora MySQL and AWS Aurora 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
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
How it works
How to connect AWS Aurora MySQL to AWS Aurora 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.
- 01
Connect your apps
Authenticate AWS Aurora MySQL and AWS Aurora PostgreSQL with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
AWS Aurora MySQL
connected
AWS Aurora PostgreSQL
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the AWS Aurora MySQL and AWS Aurora 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.
objects · AWS Aurora MySQL ⇄ AWS Aurora PostgreSQLCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
AWS Aurora MySQL AWS Aurora PostgreSQLCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
AWS Aurora MySQL and AWS Aurora PostgreSQL integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between AWS Aurora MySQL and AWS Aurora PostgreSQL: authenticate both systems, choose the objects to sync (such as AWS Aurora MySQL's Tables and Rows), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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Common patterns for AWS Aurora MySQL and AWS Aurora PostgreSQL: Cross-engine replication; Migration without a hard cutover; Shared reference data. Aurora MySQL Tables and Rows sync into Aurora PostgreSQL Tables continuously in both directions.
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AWS Aurora MySQL: SQL wire protocol (MySQL-compatible), standard MySQL drivers and JDBC. Authentication: Database credentials, optionally AWS IAM database authentication, over TLS. AWS Aurora PostgreSQL: SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC. Authentication: Database credentials, optionally AWS IAM database authentication, over TLS. Stacksync manages authentication, retries, and rate limits on both sides.
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AWS Aurora MySQL: Read replicas share the cluster storage volume, letting syncs read from a replica endpoint without adding load to the writer. AWS Aurora PostgreSQL: Replication slots retain WAL for their consumers, so an interrupted CDC sync can resume without losing changes. Stacksync's field mapping accounts for these differences between AWS Aurora MySQL and AWS Aurora PostgreSQL without custom code.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means AWS Aurora MySQL and AWS Aurora PostgreSQL records are not retained after a sync operation.
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.
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 398 integrations available for AWS Aurora MySQL and AWS Aurora PostgreSQL.
Popular · 5 of 398
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AWS Aurora MySQL ⇄ InfluxDB -
AWS Aurora MySQL ⇄ InterSystems IRIS -
AWS Aurora PostgreSQL ⇄ Postgres Heroku -
AWS Aurora PostgreSQL ⇄ Airtable -
AWS Aurora PostgreSQL ⇄ MongoDB
No integrations match .
