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Data warehouse

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

Keep BigQuery and MotherDuck 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 MotherDuck

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

Data teams connect BigQuery and MotherDuck to move data between two warehouses with different strengths: BigQuery's partitioned and clustered Tables for large-scale workloads, and MotherDuck's Databases and attached local DuckDB databases for fast interactive analysis. Syncing keeps Tables and Schemas consistent so both engines query the same facts.

Stacksync syncs tables between BigQuery and MotherDuck 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 Let analysts explore BigQuery-scale data in MotherDuck for fast interactive analysis.
  • 02 Keep MotherDuck Views and BigQuery clustered Tables serving identical numbers to different tools.
  • 03 Migrate workloads between the two warehouses incrementally with continuous Table sync.
  • 04 Land CRM and ERP records in BigQuery continuously so dashboards reflect business systems without nightly batch jobs

Common sync patterns

Warehouse mirroring

BigQuery Tables replicate into MotherDuck Databases for low-latency interactive queries.

Local-to-cloud promotion

data landed in attached local DuckDB databases syncs up through MotherDuck into BigQuery Datasets.

Shared datasets

MotherDuck Database Shares stay aligned with the governed BigQuery Project of record.

What you can sync between BigQuery and MotherDuck

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 MotherDuck objects How this pairing syncs
Tables The syncable unit: only tables can be synced per the Stacksync docs. Tables The main landing target for synced records and source for analysis. 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. Databases Cloud-hosted DuckDB databases that scope a sync's reads and writes. Datasets is specific to BigQuery and Databases to MotherDuck — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Schemas Namespaces within a database used to organize synced tables. Projects is specific to BigQuery and Schemas to MotherDuck — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Views Modeled projections used as outbound sync sources. Partitioned tables is specific to BigQuery and Views to MotherDuck — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Database Shares Read-only copies of a database shared with other users or teams. Clustered tables is specific to BigQuery and Database Shares to MotherDuck — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and MotherDuck

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

MotherDuck BigQuery Interval-based propagation

DetectionStacksync polls MotherDuck for changes on an incremental schedule, reading only records changed since the previous pass. Polling.

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.
  • MotherDuck: Subject to the platform's compute and concurrency limits rather than per-request API rate limits.
What ships with BigQuery ⇄ MotherDuck

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or MotherDuck 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 MotherDuck record.

Observability

Monitoring

Track your BigQuery ⇄ MotherDuck 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 MotherDuck.

How the BigQuery and MotherDuck 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

MotherDuck

Integration surface
SQL through DuckDB clients and drivers using a MotherDuck (md:) connection
Authentication
Access token created in MotherDuck (Settings > General > Create Token), pasted into Stacksync; database name and schema configurable if not using defaults
Change detection
Polling; no log-based CDC or webhook surface is exposed
Capabilities
read · write
Rate limits
Subject to the platform's compute and concurrency limits rather than per-request API rate limits
MotherDuck setup guide
How it works

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

    Choose tables

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

BigQuery and MotherDuck 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
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:

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 390 integrations available for BigQuery and MotherDuck.

Popular · 8 of 390
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