BigQuery
Read and write support varies by record
Record types covered in the setup guide
| Record types | Coverage and requirements |
|---|---|
| See connector requirements. Confirm field permissions and sync direction. |
Data warehouse · Two-way sync platform
BigQuery and MotherDuck integration — two-way sync
Keep supported BigQuery and MotherDuck records aligned with two-way sync. Give each team access to current data while controlling which system can update each field.
- Field mappings and sync direction under your control
- Inspect and resolve record errors in one dashboard
Built for teams where self-serve, reliability and scale matter
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Example workflow
Plan table mappings between BigQuery and MotherDuck
Validate the selected objects and operations even where connector-level direction is documented.
Starting eventA selected source table needs an operational replica, a reporting projection, or a migration copy.
Choose actual tables in BigQuery and MotherDuck and compare their schemas and record types.
Specify the primary/business key, type conversions, filter boundaries, relationship dependencies, and the source of each writable field.
Plan two-way sync around connector support and your update rules; use a source-to-destination copy for read-only records. Keep source views, aggregate outputs, and writable base tables distinct.
What to verifyCounts reconcile within identical filters; updates preserve keys; precision, nulls, deletes, and schema changes follow the agreed mapping rules.
Review records and field ownershipMapping essentials
Records and field ownership
Match records by stable IDs and assign an owner for each field. The record notes identify the coverage to check.
Download the mapping worksheetCSV · No email required
Choose the tables you need in BigQuery and MotherDuck, then map their keys, field types, and filters. Your schemas determine how records relate and which system should own each field. Use the worksheet to document those choices before testing the first load.
Why teams connect BigQuery and MotherDuck
Put your BigQuery and MotherDuck data to work
Start with the work your team needs to complete: keep records up to date, make operational data available for reporting, or move data to a new system. Choose one BigQuery and MotherDuck workflow to test, define what success looks like, and decide who handles failed updates.
Let analysts explore BigQuery-scale data in MotherDuck for fast interactive analysis.
Keep MotherDuck Views and BigQuery clustered Tables serving identical numbers to different tools.
Migrate workloads between the two warehouses incrementally with continuous Table sync.
What records can you sync?
Start with the records your workflow needs. Check each system’s read and write requirements before mapping fields.
MotherDuck
Read and write support varies by record
Record types covered in the setup guide
| Record types | Coverage and requirements |
|---|---|
| See connector requirements. Confirm field permissions and sync direction. |
Connection essentials
Review how each system connects, detects changes, and permits access to your records.
View setup requirements and limits| Connection requirement | BigQuery | MotherDuck |
|---|---|---|
| Integration interface | GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs | SQL through DuckDB clients and drivers using a MotherDuck (md:) connection |
| Authentication | Dedicated Google Cloud service account and JSON key | Access token created in MotherDuck |
| Change detection | The setup provisions notification services using Eventarc and Cloud Run in the customer Google Cloud project. | The saved Stacksync guide does not specify the change-detection mechanism. |
| Read access | Available for supported records | Available for supported records |
| Write access | Available for supported records | Available for supported records |
Enterprise controls
Security and control for your integrations
Explore security controlsCompliance and data transfers
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
- DPF US-EU-UK-CH
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:
Record-level recovery
Inspect sync errors and use retry and revert controls to resolve failed updates.
Read the recovery guideImplementation
Technical reference
Review setup, record relationships, testing, and recovery for your implementation.
Authentication, permissions and API limits
Connection requirements and limits
BigQuery
- Integration interface
- GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
- Authentication
- Dedicated Google Cloud service account and JSON key; enable the APIs and grant the roles in the authorization guide.
- Change detection
- The setup provisions notification services using Eventarc and Cloud Run in the customer Google Cloud project.
- Read access
- Available for supported records
- Write access
- Available for supported records
Setup requirements
- Enable BigQuery, Cloud Run, Cloud Resource Manager, and Eventarc APIs in the target project.
- Create a dedicated service account with the roles in the authorization guide and supply its credentials through Stacksync.
Limitations to check
- Only tables are supported in the saved guide; ordinary and materialized views are excluded.
- Google Cloud quotas and the selected write method affect capacity; validate the current project limits before a large backfill.
Technical documentation
Documentation reviewed 2026-09-15. Check the linked guides for current account and record requirements.
MotherDuck
- Integration interface
- 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
- The saved Stacksync guide does not specify the change-detection mechanism. Confirm it for the selected objects.
- Read access
- Available for supported records
- Write access
- Available for supported records
Setup requirements
- Generate a MotherDuck access token in Settings > General and enter it in the Stacksync connection form.
- Specify the intended database and schema and confirm read/write coverage for the selected tables.
Limitations to check
- Two-way sync is documented at connector level. Confirm table eligibility, writable columns, and the change-detection interval during setup.
Technical documentation
Documentation reviewed 2026-09-15. Check the linked guides for current account and record requirements.
Prepare BigQuery and MotherDuck access
Set up both accounts before testing the mapping. Use test records where available, and identify the account administrator who can approve access and help resolve setup errors.
BigQuery setup checklist
- Enable BigQuery, Cloud Run, Cloud Resource Manager, and Eventarc APIs in the target project.
- Create a dedicated service account with the roles in the authorization guide and supply its credentials through Stacksync.
Setup guides: Authorize BigQuery
MotherDuck setup checklist
- Generate a MotherDuck access token in Settings > General and enter it in the Stacksync connection form.
- Specify the intended database and schema and confirm read/write coverage for the selected tables.
Prepare to go live
Record the fields each system can update, the first-load cutoff, both record IDs, the expected update delay, and who handles errors. Complete the tests before production before expanding to more records.
Use the BigQuery and MotherDuck planning worksheet to capture these decisions. Record the access owner in the worksheet and enter credentials only in the connection setup.
Record identity and field ownership
Keep each record tied to its source ID. Use the references below to choose field owners and preserve relationships between records.
Download the mapping worksheet · CSV, no email required
Choose the tables you need in BigQuery and MotherDuck, then map their keys, field types, and filters. Your schemas determine how records relate and which system should own each field. Use the worksheet to document those choices before testing the first load.
Compare integration approaches
Choose your tables and update rules before the integration method. A live application copy, a reporting table, and a one-time migration need different handling for updates, history, deletes, and reconciliation.
Stacksync managed sync
- Best fit
- Two-way sync for supported objects. Choose the records, writable fields, and permissions for your workflow.
- Operating responsibility
- Fits ongoing record synchronization when the required operations are supported. Add workflow steps for approvals or business actions that go beyond copying fields.
- Before you choose
- Provide your table or object schemas and one example update; confirm the supported keys, fields, and direction.
Native vendor integration
- Best fit
- A vendor-built integration may fit if it supports your BigQuery and MotherDuck record types.
- Operating responsibility
- Can reduce setup for a supported workflow. You may need another method for records or business steps it does not cover.
- Before you choose
- First check whether either vendor offers this integration. If available, verify your record types, update direction, account tier, and related-record handling.
Custom API or workflow
- Best fit
- Consider when BigQuery and MotherDuck need a transformation, approval, or action outside a direct record sync.
- Operating responsibility
- Provides control over business steps; the team owns credentials, version changes, error queues, and reconciliation.
- Before you choose
- Verify endpoint permissions, pagination, quotas, duplicate detection, and failure recovery.
File or scheduled snapshot
- Best fit
- Consider for a one-time BigQuery / MotherDuck migration or a reporting need with an explicit freshness window.
- Operating responsibility
- Can simplify a bounded transfer; later changes and deletion history require another extraction or a separately designed incremental process.
- Before you choose
- Record the extraction cutoff, source IDs, encoding, date/number formats, and reconciliation totals.
Workflow scenarios and expected results
Plan table mappings between BigQuery and MotherDuck
Validate the selected objects and operations even where connector-level direction is documented.
Starting event: A selected source table needs an operational replica, a reporting projection, or a migration copy.
- Choose actual tables in BigQuery and MotherDuck and compare their schemas and record types.
- Specify the primary/business key, type conversions, filter boundaries, relationship dependencies, and the source of each writable field.
- Plan two-way sync around connector support and your update rules; use a source-to-destination copy for read-only records. Keep source views, aggregate outputs, and writable base tables distinct.
Expected result: Counts reconcile within identical filters; updates preserve keys; precision, nulls, deletes, and schema changes follow the agreed mapping rules.
If it fails: Compare current source state, key mapping, and destination constraints before retrying. Reconcile the backlog after any schema or permission change.
Initial load and acceptance testing
Keep both record IDs with the expected and actual result. Reconcile the same filters and time window in each system.
Direction and permissions
Test case
Check that each connected account can read and write the chosen objects. Exercise both directions with a test record before enabling production changes.
Expected result
Only an approved, supported direction and permitted fields are written.
Schema, keys, and reconciliation
Test case
Use an actual BigQuery and MotherDuck table. Compare key uniqueness, nulls, decimal precision, timezone conversions, and counts within identical filters.
Expected result
The process meets its agreed freshness target and reconciliation has no unexplained differences.
Failed updates, retries and recovery
Start with the failed record and the destination error, then inspect the source value, field requirements, and access.
A record type or update is unavailable
Investigate
Check the BigQuery and MotherDuck connector guides, account permissions, and any operations marked On Request.
Next action
Ask the integration team to confirm a supported way to handle that record. Verify whether it needs connector configuration or a separate workflow step.
Source and destination disagree after a retry
Investigate
Compare current source values, destination validation, identity mappings, and any side effects already completed.
Next action
Stacksync issue retry reads the current source state. Decide the intended state before retrying or reverting; reconcile downstream effects separately.
Read the Stacksync issues dashboard guide for retry and revert behavior.
Change detection and update delivery
How updates move between BigQuery and MotherDuck
See how each system detects changes and which updates the other system can receive. Each direction has its own permissions and record requirements.
BigQuery MotherDuck Timing depends on the connected systems
Detect changesThe setup provisions notification services using Eventarc and Cloud Run in the customer Google Cloud project.
Apply updatesSelected writable fields update in MotherDuck. Its permissions and validation rules still apply; rejected records can be inspected in the Issues dashboard.
MotherDuck BigQuery Timing depends on the connected systems
Detect changesThe saved Stacksync guide does not specify the change-detection mechanism. Confirm it for the selected objects.
Apply updatesSelected writable fields update in BigQuery. Its permissions and validation rules still apply; rejected records can be inspected in the Issues dashboard.
Update timing and record limits
- Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.
- Google Cloud quotas and the selected write method affect capacity; validate the current project limits before a large backfill.
- Two-way sync is documented at connector level. Confirm table eligibility, writable columns, and the change-detection interval during setup.
FAQ
BigQuery and MotherDuck integration FAQ
-
Stacksync connects BigQuery and MotherDuck with two-way sync for supported records. Authorize the accounts, select the record types, and map compatible fields. Both connectors support reading and writing; object permissions determine the available fields. Use the Issues dashboard to inspect and resolve rejected updates.
-
Stacksync supports two-way sync between writable records in both systems. The chosen objects and fields must permit updates in both directions. Read-only fields can supply values but cannot receive updates. Stacksync can read database views; it does not write back to the view.
-
Prepare both accounts, the selected object schemas, stable source and destination IDs, and the expected outcome. Enable BigQuery, Cloud Run, Cloud Resource Manager, and Eventarc APIs in the target project. Generate a MotherDuck access token in Settings > General and enter it in the Stacksync connection form. Use the pair worksheet to record ownership and acceptance criteria.
-
Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.
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No. Stacksync documents that pre-existing duplicates are not merged automatically when two-way sync begins. Review the initial dataset and matching plan before enabling it; an empty destination can simplify the first load.
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Start with one business entity and a stable record ID. Map a small set of editable fields with compatible types, test required values and relationships, then expand after the pilot passes.
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Check the destination error, field constraints, permissions, and current source value. The Stacksync issues dashboard supports retry and revert; retry reads current source values, so verify the intended record state before acting.
-
Use the current Stacksync pricing page and confirm the supported implementation with the team. Scope the required objects, record volume, update frequency, initial load, and support needs when comparing a managed connector with native or custom development.
-
Choose the tables you need in BigQuery and MotherDuck, then map their keys, field types, and filters. Your schemas determine how records relate and which system should own each field. Use the worksheet to document those choices before testing the first load.
-
Choose your tables and update rules before the integration method. A live application copy, a reporting table, and a one-time migration need different handling for updates, history, deletes, and reconciliation.
Integration directory
Related integrations
Review documented support, sync direction, and setup requirements on each pair page. Search all 485 integrations listed for BigQuery and MotherDuck.
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Next step
See your workflow in Stacksync
Walk through your BigQuery and MotherDuck records, field mappings, and requirements with an integration engineer.
