Skip to content
Database

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

Keep AWS Aurora MySQL and MongoDB 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 MongoDB

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

Teams sync AWS Aurora MySQL with MongoDB when part of the stack is relational and part is document-based. Aurora tables and rows map to MongoDB collections and documents, so services built on either model read consistent data without dual-write logic in application code.

Stacksync syncs tables or collections between AWS Aurora MySQL and MongoDB 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 Serve a MongoDB-backed API from data mastered in Aurora MySQL tables.
  • 02 Run SQL analytics over MongoDB documents by materializing them as Aurora rows.
  • 03 Migrate a service between document and relational storage with both databases kept current.
  • 04 Sync a production Aurora cluster with an analytics database while filtering out sensitive columns.

Common sync patterns

Relational-to-document feed

Aurora MySQL rows replicate into MongoDB documents for services that consume JSON-shaped data.

Document flattening

MongoDB documents, including embedded documents and arrays, sync into Aurora tables and columns for SQL reporting.

Two-way collection sync

changes in MongoDB collections and Aurora tables propagate both directions with primary keys and indexes preserved.

What you can sync between AWS Aurora MySQL and MongoDB

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 MongoDB objects How this pairing syncs
Views Can serve as read-only sync sources for derived or filtered datasets. Views Read-only aggregation-defined sources for filtered sync datasets. 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. Change streams The oplog-backed event feed that powers real-time change capture. Columns is specific to AWS Aurora MySQL and Change streams to MongoDB — each maps to any object or custom field on the other side.
Primary keys and indexes Used to match rows across systems and keep incremental syncs efficient. GridFS files Chunked file storage whose metadata can be referenced by synced documents. Primary keys and indexes is specific to AWS Aurora MySQL and GridFS files to MongoDB — 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. Databases Logical groupings of collections that scope a sync connection. Foreign keys is specific to AWS Aurora MySQL and Databases to MongoDB — 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. Collections The table-like sync unit; each collection maps to a table or object in the paired system. Stored procedures and triggers is specific to AWS Aurora MySQL and Collections to MongoDB — 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. Documents BSON records created, updated, and deleted during syncs, keyed by _id. Databases (schemas) is specific to AWS Aurora MySQL and Documents to MongoDB — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora MySQL and MongoDB

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

MongoDB AWS Aurora MySQL Sub-second propagation

DetectionChanges in MongoDB are captured at the source via change data capture — no polling loop against its API. MongoDB oplog and change streams (requires the database to run as a replica set — even single-node).

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 ⇄ MongoDB

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

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

Real-time

Two-way sync

Changes in AWS Aurora MySQL or MongoDB 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 MongoDB 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 MongoDB record.

Observability

Monitoring

Track your AWS Aurora MySQL ⇄ MongoDB 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 MongoDB.

How the AWS Aurora MySQL and MongoDB 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

MongoDB

Integration surface
MongoDB wire protocol via official drivers; Atlas additionally offers an administration REST API for cluster management
Authentication
Database credentials (username/password) or TLS/SSL X.509 certificate (.pem upload), entered individually or via a MongoDB connection string (SRV or standard); Stacksync IP allowlisting required
Change detection
MongoDB oplog and change streams (requires the database to run as a replica set — even single-node); Stacksync leverages these built-in tools to track changes in real time
Capabilities
read · write · CDC
MongoDB setup guide
How it works

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

    Choose tables

    Pick the AWS Aurora MySQL and MongoDB 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 ⇄ MongoDB
    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 MongoDB
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

AWS Aurora MySQL and MongoDB 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 484 integrations available for AWS Aurora MySQL and MongoDB.

Popular · 6 of 484
All AWS Aurora MySQL integrations All MongoDB 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