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Google Cloud SQL to MongoDB integration — real-time, two-way sync

Keep Google Cloud SQL 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.

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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Google Cloud SQL and MongoDB

Keep Google Cloud SQL and MongoDB synchronized in real time, across engines, regions, or services, in one or both directions.

Two databases that must agree is one of the oldest problems in engineering: different engines for different workloads, separate services with overlapping reference data, a migration in flight, or regional instances that share a subset of records. Hand-rolled replication across systems means change capture, conflict handling, and type mapping, all built and maintained by your team.

Stacksync syncs tables or collections between Google Cloud SQL 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 Migrate from a self-managed database by syncing Cloud SQL and the legacy system during cutover.
  • 02 Keep an internal admin application backed by Cloud SQL consistent with an ERP or billing system.
  • 03 Sync MongoDB collections with a CRM so customer documents written by the application appear as CRM records, and CRM edits flow back as document updates.
  • 04 Replicate operational MongoDB data into a relational database, flattening nested documents into normalized tables for SQL reporting.

Common sync patterns

Regional or environment copies

Mirror selected tables to another region or environment continuously, filtered to just the rows that should travel.

Cross-engine sync

Keep the same dataset live in both Google Cloud SQL and MongoDB, so each workload runs on the engine that suits it.

Migration with zero-downtime cutover

When one database is replacing the other, sync both directions during the transition and switch traffic when ready, without a freeze window.

What you can sync between Google Cloud SQL 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.

Google Cloud SQL objects MongoDB objects How this pairing syncs
Databases Scope the tables included in a sync configuration. Databases Logical groupings of collections that scope a sync connection. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Views Read-only sources for shaping data before syncing it out. 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.
Instances The managed MySQL, PostgreSQL, or SQL Server server a sync connects to. Collections The table-like sync unit; each collection maps to a table or object in the paired system. Instances is specific to Google Cloud SQL and Collections to MongoDB — each maps to any object or custom field on the other side.
Schemas Namespace tables in PostgreSQL and SQL Server instances. Documents BSON records created, updated, and deleted during syncs, keyed by _id. Schemas is specific to Google Cloud SQL and Documents to MongoDB — each maps to any object or custom field on the other side.
Tables Mapped directly to sync targets; schema changes can be propagated. Embedded documents and arrays Nested structures that syncs flatten or map to related records in relational targets. Tables is specific to Google Cloud SQL and Embedded documents and arrays to MongoDB — each maps to any object or custom field on the other side.
Rows Read and written by primary key during each sync cycle. Indexes Keep lookups by sync key fast on large collections. Rows is specific to Google Cloud SQL and Indexes to MongoDB — each maps to any object or custom field on the other side.

How changes propagate between Google Cloud SQL 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.

Google Cloud SQL MongoDB Sub-second propagation

DetectionChanges in Google Cloud SQL are captured at the source via change data capture — no polling loop against its API. Engine-dependent log-based CDC: MySQL binlog, PostgreSQL logical replication, SQL Server change tracking.

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

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

Rate-limit considerations

  • Google Cloud SQL: Constrained by instance size and connection limits rather than API quotas.
What ships with Google Cloud SQL ⇄ MongoDB

Connect Google Cloud SQL and MongoDB for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Google Cloud SQL–MongoDB connection.

Real-time

Two-way sync

Changes in Google Cloud SQL or MongoDB instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Google Cloud SQL 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 Google Cloud SQL or MongoDB record.

Observability

Monitoring

Track your Google Cloud SQL ⇄ MongoDB sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Google Cloud SQL and MongoDB.

How the Google Cloud SQL and MongoDB connectors work

Google Cloud SQL

Integration surface
Native SQL wire protocols (MySQL, PostgreSQL, SQL Server) plus a REST admin API for instance management
Authentication
Database credentials; IAM database authentication is available for MySQL and PostgreSQL
Change detection
Engine-dependent log-based CDC: MySQL binlog, PostgreSQL logical replication, SQL Server change tracking; polling as a fallback
Capabilities
read · write · CDC
Rate limits
Constrained by instance size and connection limits rather than API quotas.

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 Google Cloud SQL 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 Google Cloud SQL 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
    Google Cloud SQL connected
    MongoDB connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Google Cloud SQL 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 · Google Cloud SQL ⇄ 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
    Google Cloud SQL MongoDB
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Google Cloud SQL 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 469 integrations available for Google Cloud SQL and MongoDB.

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