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Data warehouse

BigQuery to Google Cloud Platform integration — real-time, two-way sync

Keep BigQuery and Google Cloud Platform 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 BigQuery and Google Cloud Platform

Keep tables consistent across BigQuery and Google Cloud Platform, for a migration, a multi-warehouse stack, or a dataset two platforms both need.

Companies end up with two warehouses for practical reasons: a migration in progress, teams that standardized on different platforms, an acquisition, or tools that only connect to one of them. The result is the same dataset maintained twice, with duplicated pipelines and numbers that almost match.

Stacksync syncs tables between BigQuery and Google Cloud Platform continuously, in either or both directions. Rows changed on one platform appear on the other within seconds, with schema and type mapping handled, so both warehouses answer questions with the same data.

Common use cases

  • Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
  • Feed ML feature tables in BigQuery from operational systems on a continuous schedule
  • Publish change events to Pub/Sub so downstream services react to record updates as they happen.
  • Sync operational CRM and ERP data into BigQuery for analytics without building custom pipelines.

Migration without a big bang

When one platform is replacing the other, keep tables mirrored while workloads move over gradually, and cut over with nothing to backfill.

Serve tools that only connect to one platform

Mirror the datasets a BI tool, notebook, or application needs onto the platform it can actually reach.

Shared datasets across teams

Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.

What you can sync between BigQuery and Google Cloud Platform

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 Google Cloud Platform objects
Datasets Organizational container — you pick which dataset’s tables to sync. Firestore documents Document data read and written through the Firestore API for app-facing syncs.
Projects Connection scope: the service account grants access per project. Spanner tables Strongly consistent relational tables accessed via SQL for transactional workloads.
Tables The syncable unit: only tables can be synced per the Stacksync docs. BigQuery datasets Namespaces that group tables; syncs target tables within a dataset.
Partitioned tables Synced like regular tables; partition columns map to target fields. BigQuery tables The primary analytics destination, written through load jobs or the Storage Write API and queried with SQL.
Clustered tables Supported; clustering is transparent to the sync. Cloud SQL databases Managed Postgres, MySQL, and SQL Server instances synced like ordinary relational databases.
What ships with BigQuery ⇄ Google Cloud Platform

Connect BigQuery and Google Cloud Platform for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Google Cloud Platform 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 Google Cloud Platform record.

Observability

Monitoring

Track your BigQuery ⇄ Google Cloud Platform 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 Google Cloud Platform.

How the BigQuery and Google Cloud Platform 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

Google Cloud Platform

Integration surface
Per-service REST and gRPC APIs; BigQuery speaks SQL and Cloud SQL exposes standard database wire protocols
Authentication
IAM service accounts with OAuth 2.0 tokens
Change detection
Varies by service: log-based CDC on Cloud SQL (logical replication or binlog, also via Datastream), Pub/Sub for event delivery, polling for BigQuery tables
Capabilities
read · write · CDC · webhooks
Rate limits
Quotas are set per service and per project; BigQuery, Pub/Sub, and Cloud SQL each enforce their own limits
How it works

How to connect BigQuery to Google Cloud Platform — 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 Google Cloud Platform 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
    Google Cloud Platform connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the BigQuery and Google Cloud Platform 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 ⇄ Google Cloud Platform
    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 Google Cloud Platform
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
FAQ

BigQuery and Google Cloud Platform integration FAQ

SECURITY

Security teams love 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
CSA STAR
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 386 integrations available for BigQuery and Google Cloud Platform.

Popular · 8 of 386
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