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Data warehouse

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

Keep BigQuery and Snowflake 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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Why teams connect BigQuery and Snowflake

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

Data teams sync BigQuery and Snowflake when the two warehouses coexist — after an acquisition, across departments, or during a migration. Tables and Schemas replicate between the platforms so each side queries current data without duplicate pipeline builds.

Stacksync syncs tables between BigQuery and Snowflake 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

  • 01 Keep BigQuery and Snowflake Tables consistent during a phased warehouse migration.
  • 02 Let GCP-based teams query Snowflake-owned Schemas from their own BigQuery Projects.
  • 03 Consolidate reporting when different departments own different warehouses.
  • 04 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources

Common sync patterns

Cross-warehouse replication

BigQuery Datasets and Tables mirror into Snowflake Databases and Schemas with schema changes propagated.

Change capture

Snowflake Streams feed updates into partitioned BigQuery Tables for near-real-time parity.

View sharing

Materialized Views built in Snowflake replicate as BigQuery Tables for teams standardized on GCP.

What you can sync between BigQuery and Snowflake

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 Snowflake objects How this pairing syncs
Tables The syncable unit: only tables can be synced per the Stacksync docs. Tables The main landing and activation target for synced records. 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 results synced outward for low-latency reads. Datasets is specific to BigQuery and Materialized Views to Snowflake — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Streams Row-level change records on a table, consumed to process deltas instead of full scans. Projects is specific to BigQuery and Streams to Snowflake — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Stages File staging areas used for bulk loads into synced tables. Partitioned tables is specific to BigQuery and Stages to Snowflake — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Tasks Scheduled SQL used to transform synced data after it lands. Clustered tables is specific to BigQuery and Tasks to Snowflake — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Snowflake

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

Snowflake BigQuery Sub-second propagation

DetectionChanges in Snowflake are captured at the source via change data capture — no polling loop against its API. The setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism.

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.
  • Snowflake: No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time.
What ships with BigQuery ⇄ Snowflake

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Snowflake 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 Snowflake record.

Observability

Monitoring

Track your BigQuery ⇄ Snowflake 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 Snowflake.

How the BigQuery and Snowflake 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

Snowflake

Integration surface
SQL via JDBC/ODBC and native drivers, plus the Snowflake SQL REST API
Authentication
Dedicated Snowflake service user + role with RSA key-pair authentication (Stacksync-provided public key), created via a setup script requiring SECURITY_ADMIN and ACCOUNTADMIN roles
Change detection
Not explicitly stated; the setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism
Capabilities
read · write · CDC
Rate limits
No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time
Snowflake setup guide
How it works

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

    Choose tables

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

BigQuery and Snowflake 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 583 integrations available for BigQuery and Snowflake.

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