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Data warehouse ⇄ Database

BigQuery to PostgreSQL integration — real-time, two-way sync

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

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Why teams connect BigQuery and PostgreSQL

Connect PostgreSQL and BigQuery with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.

Engineering and data teams pair PostgreSQL, the operational database, with BigQuery, the analytical warehouse. Postgres Tables and Materialized Views replicate into BigQuery Datasets so analysts query production data at scale without adding load to the transactional database.

Stacksync covers both directions with one connection. Tables or collections in PostgreSQL sync into BigQuery in real time, and result tables in BigQuery sync back into PostgreSQL, with schema and type mapping between the two systems handled for you.

Common use cases

  • 01 Run warehouse-scale queries on production Postgres data without impacting the primary database.
  • 02 Preserve Primary and Unique Keys and Columns end to end so BigQuery models match the source schema.
  • 03 Serve BigQuery-computed metrics from PostgreSQL to keep application reads low-latency.
  • 04 Feed ML feature tables in BigQuery from operational systems on a continuous schedule

Common sync patterns

Operational replication

PostgreSQL Tables and Schemas mirror into BigQuery Datasets with schema changes propagated automatically.

Analytics offload

Postgres Views materialize as partitioned BigQuery Tables for heavy analytical queries.

Reverse ETL

aggregates computed in clustered BigQuery Tables sync back into PostgreSQL Tables that serve the application.

What you can sync between BigQuery 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.

BigQuery objects PostgreSQL objects How this pairing syncs
Tables The syncable unit: only tables can be synced per the Stacksync docs. 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.
Datasets Organizational container — you pick which dataset’s tables to sync. Materialized Views Precomputed result sets synced outward on a refresh schedule. Datasets is specific to BigQuery and Materialized Views to PostgreSQL — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Schemas Namespaces that scope which tables a sync reads and writes. Projects is specific to BigQuery and Schemas to PostgreSQL — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Columns Field-level mapping targets; types are mapped to the connected system's field types. Partitioned tables is specific to BigQuery and Columns to PostgreSQL — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Primary and Unique Keys Used as match keys for idempotent upserts and conflict resolution. Clustered tables is specific to BigQuery and Primary and Unique Keys to PostgreSQL — each maps to any object or custom field on the other side.

How changes propagate between BigQuery 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.

BigQuery PostgreSQL Sub-second propagation

DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").

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

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

Rate-limit considerations

  • BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
  • PostgreSQL: No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput.
What ships with BigQuery ⇄ PostgreSQL

Connect BigQuery and PostgreSQL for flexible, real-time data sync.

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

Real-time

Two-way sync

Changes in BigQuery or PostgreSQL instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery 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 BigQuery or PostgreSQL record.

Observability

Monitoring

Track your BigQuery ⇄ PostgreSQL sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between BigQuery and PostgreSQL.

How the BigQuery and PostgreSQL connectors work

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide

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 BigQuery 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 BigQuery 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
    BigQuery connected
    PostgreSQL connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

BigQuery 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 581 integrations available for BigQuery and PostgreSQL.

Popular · 8 of 581
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