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

Keep Google Cloud SQL 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.

  • 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

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Why teams connect Google Cloud SQL and PostgreSQL

Keep Google Cloud SQL and PostgreSQL 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 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 Keep an internal admin application backed by Cloud SQL consistent with an ERP or billing system.
  • 02 Migrate from a self-managed database by syncing Cloud SQL and the legacy system during cutover.
  • 03 Let an application write to its own database and have those rows appear as records in business systems in near real time
  • 04 Consolidate data from several microservice databases into one operational Postgres store

Common sync patterns

Shared reference data between services

Services that own separate databases stay consistent on the records they share, without a custom replication layer.

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 PostgreSQL, so each workload runs on the engine that suits it.

What you can sync between Google Cloud SQL 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.

Google Cloud SQL objects PostgreSQL objects How this pairing syncs
Schemas Namespace tables in PostgreSQL and SQL Server instances. Schemas Namespaces that scope which tables a sync reads and writes. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Tables Mapped directly to sync targets; schema changes can be propagated. 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.
Views Read-only sources for shaping data before syncing it out. 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.
Transaction logs MySQL binlog or PostgreSQL WAL, the source for log-based change capture. Custom Types and Enums Constrain synced values to a fixed set, mirroring picklist fields. Transaction logs is specific to Google Cloud SQL and Custom Types and Enums to PostgreSQL — each maps to any object or custom field on the other side.
Instances The managed MySQL, PostgreSQL, or SQL Server server a sync connects to. Materialized Views Precomputed result sets synced outward on a refresh schedule. Instances is specific to Google Cloud SQL and Materialized Views to PostgreSQL — each maps to any object or custom field on the other side.
Databases Scope the tables included in a sync configuration. Columns Field-level mapping targets; types are mapped to the connected system's field types. Databases is specific to Google Cloud SQL and Columns to PostgreSQL — each maps to any object or custom field on the other side.

How changes propagate between Google Cloud SQL 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.

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

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

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

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

Real-time

Two-way sync

Changes in Google Cloud SQL or PostgreSQL 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 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 Google Cloud SQL or PostgreSQL record.

Observability

Monitoring

Track your Google Cloud SQL ⇄ PostgreSQL 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 PostgreSQL.

How the Google Cloud SQL and PostgreSQL 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.

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

    Choose tables

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

Google Cloud SQL 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 474 integrations available for Google Cloud SQL and PostgreSQL.

Popular · 4 of 474
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