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 / Business productivity · Two-way sync platform
BigQuery and Front integration — two-way sync
Keep supported BigQuery and Front 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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Migrated from Fivetran
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Migrated from Celigo
Example workflow
Message or conversation reporting workflow
Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.
Starting eventA change involving the proposed message or conversation table in BigQuery or Front Conversations needs a defined result in the other system.
Start with the proposed message or conversation table in BigQuery and Front Conversations. Use the record-matching and field-ownership rules from your mapping worksheet.
Resolve conversation, participant, and customer context before associating messages.
Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.
What to verifyTest a delivery-state change, repeated message, private conversation, and reply linked to the correct thread.
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
| BigQuery record | Front record | Record matching | Field ownership |
|---|---|---|---|
| Proposed message or conversation tableProposed table; choose its name and schema.Reporting dataset | ConversationsProposed record; confirm Stacksync object support. | Retain message ID, conversation/thread ID, channel, and sender identity. | Separate message history from actions that send new messages; preserve private/public visibility and channel consent. |
| Proposed contact tableProposed table; choose its name and schema.Reporting dataset | ContactsDocumented record: Supported | Use a stable person/contact ID and an explicit cross-system lookup. Email can change and can be shared, so treat it as a matching clue rather than a universal key. | Keep consent and communication preferences under an agreed authority; a general contact update must not silently resubscribe someone. |
| Proposed company tableProposed table; choose its name and schema.Reporting dataset | AccountsDocumented record: Supported | Retain the source company ID and the destination customer/company ID. Separate legal entities, subsidiaries, and business units; a shared name or web domain is insufficient. | Assign ownership separately for relationship details and finance-controlled billing details. |
Use writable fields from the connected accounts. Read values from read-only fields without writing changes back to them, and define deletion handling separately.
Why teams connect BigQuery and Front
Put your BigQuery and Front 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 Front workflow to test, define what success looks like, and decide who handles failed updates.
Report first-response and resolution times across every Front Inbox from a single BigQuery Dataset.
Join Front Conversations with revenue tables in BigQuery to quantify support cost per Account.
Audit Comments and Messages in BigQuery for QA sampling of support interactions.
What records can you sync?
Start with the records your workflow needs. Check each system’s read and write requirements before mapping fields.
Front
Read and write support varies by record
Record types covered in the setup guide
| Record types | Coverage and requirements |
|---|---|
| ✅ Supported. 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 | Front |
|---|---|---|
| Integration interface | GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs | REST API (Core API) |
| Authentication | Dedicated Google Cloud service account and JSON key | OAuth authorization via the Stacksync UI |
| 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.
Front
- Integration interface
- REST API (Core API)
- Authentication
- OAuth authorization via the Stacksync UI ("Connections" > "create new connection" > "Front" > "Authorize") — no coding required
- 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
- Create a Front connection in Stacksync and complete the authorization flow.
- Check the documented Accounts, Contacts, Contact Groups, and Events coverage against the required use case.
Limitations to check
- Two-way sync is documented at connector level. The object list does not specify writable fields or change-detection behavior; validate both for the selected records.
Technical documentation
Documentation reviewed 2026-09-15. Check the linked guides for current account and record requirements.
Prepare BigQuery and Front 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
Front setup checklist
- Create a Front connection in Stacksync and complete the authorization flow.
- Check the documented Accounts, Contacts, Contact Groups, and Events coverage against the required use case.
Setup guides: Authorize Front
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 Front 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
Reporting dataset
Proposed message or conversation table in BigQuery (choose its name) / Conversations
Plan a message or conversation dataset while preserving its source meaning.
Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.
- BigQuery
- Your database schema
- Front
- Object support to establish
- Record identity
- Retain message ID, conversation/thread ID, channel, and sender identity.
- Field ownership
- Separate message history from actions that send new messages; preserve private/public visibility and channel consent.
Fields to include
- Source message ID
- Thread reference
- Delivery state
- Timestamp
- Related record ID
- Record dependencies
- Resolve conversation, participant, and customer context before associating messages.
- Validation
- Test a delivery-state change, repeated message, private conversation, and reply linked to the correct thread.
- Recovery
- Check provider delivery state before retrying a send; replaying history must not send the message again.
References: Front: Conversations documentation
Reporting dataset
Proposed contact table in BigQuery (choose its name) / Contacts
Plan a contact dataset while preserving its source meaning.
Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.
- BigQuery
- Your database schema
- Front
- Documented record · ✅ Supported
- Record identity
- Use a stable person/contact ID and an explicit cross-system lookup. Email can change and can be shared, so treat it as a matching clue rather than a universal key.
- Field ownership
- Keep consent and communication preferences under an agreed authority; a general contact update must not silently resubscribe someone.
Fields to include
- Source person ID
- Display name
- Email address
- Organization reference
- Consent state
- Record dependencies
- Resolve the organization relationship and any owner or consent references required by the destination.
- Validation
- Test an email change, two records sharing an email, and a person associated with multiple organizations.
- Recovery
- Hold ambiguous matches for review and reconcile the person ID before retrying; preserve the consent decision already recorded by its owner.
References: Front: Contacts documentation
Reporting dataset
Proposed company table in BigQuery (choose its name) / Accounts
Plan a company dataset while preserving its source meaning.
Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.
- BigQuery
- Your database schema
- Front
- Documented record · ✅ Supported
- Record identity
- Retain the source company ID and the destination customer/company ID. Separate legal entities, subsidiaries, and business units; a shared name or web domain is insufficient.
- Field ownership
- Assign ownership separately for relationship details and finance-controlled billing details.
Fields to include
- Source record ID
- Legal or display name
- Business-unit reference
- Lifecycle status
- Record dependencies
- Resolve parent organizations, business units, and currency references before dependent transactions.
- Validation
- Use two organizations with similar names and one with multiple business units. Verify that an update reaches the intended entity only.
- Recovery
- Repair the cross-system ID relationship before retrying dependent records; do not merge companies solely to remove a sync error.
References: Front: Accounts documentation
Reporting dataset
Proposed application user or identity table in BigQuery (choose its name) / Teammates
Plan a application user or identity dataset while preserving its source meaning.
Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.
- BigQuery
- Your database schema
- Front
- Object support to establish
- Record identity
- Use the immutable user ID within the tenant or directory. Do not equate an application user with a CRM customer contact.
- Field ownership
- Identity and application owners approve account lifecycle and access changes; synchronize only approved attributes.
Fields to include
- Source user ID
- Tenant reference
- Account status
- Group references
- Record dependencies
- Resolve tenant and group references and establish a protected administrative-account policy.
- Validation
- Test a renamed login, disabled account, missing group, and a user existing in two tenants.
- Recovery
- Review access impact before retrying a lifecycle change; reconcile current identity state and retain an approval trail.
References: Front: Teammates documentation
Reporting dataset
Proposed event or activity table in BigQuery (choose its name) / Events
Plan a event or activity dataset while preserving its source meaning.
Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.
- BigQuery
- Your database schema
- Front
- Documented record · ✅ Supported
- Record identity
- Keep the source event ID, source system, occurrence time, and ingestion time. Use an explicit duplicate-detection key.
- Field ownership
- Decide whether the destination stores an immutable history or only a current-state summary.
Fields to include
- Source event ID
- Event type
- Occurred-at time
- Related record ID
- Payload version
- Record dependencies
- Resolve the related customer, user, or transaction identity without assuming the event ID is the entity ID.
- Validation
- Deliver the same event twice, then an older event after a newer one; verify duplicate and ordering behavior.
- Recovery
- Identify side effects already completed before replaying an event; use the agreed deduplication key.
References: Front: Events documentation
Compare integration approaches
Use BigQuery Proposed message or conversation table in BigQuery (choose its name) and Front Conversations for the first pilot. Verify its identity and permitted direction, then add dependent records only when the acceptance checks pass.
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
- Check record matching: Retain message ID, conversation/thread ID, channel, and sender identity. Verify field coverage, deletion handling, and how changes are detected.
Native vendor integration
- Best fit
- A vendor-built integration may fit if it supports your BigQuery and Front 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 Proposed message or conversation table in BigQuery (choose its name) / Conversations, update direction, account tier, and related-record handling.
Custom API or workflow
- Best fit
- Consider when BigQuery and Front 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 / Front 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
Message or conversation reporting workflow
Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.
Starting event: A change to the selected Proposed message or conversation table in BigQuery (choose its name) or Conversations record needs a defined result in the other system.
- Start with BigQuery Proposed message or conversation table in BigQuery (choose its name) and Front Conversations. Use the record-matching and field-ownership rules from your mapping worksheet.
- Resolve conversation, participant, and customer context before associating messages.
- Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.
Expected result: Test a delivery-state change, repeated message, private conversation, and reply linked to the correct thread.
If it fails: Check provider delivery state before retrying a send; replaying history must not send the message again.
Contact reporting workflow
Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.
Starting event: A change to the selected Proposed contact table in BigQuery (choose its name) or Contacts record needs a defined result in the other system.
- Start with BigQuery Proposed contact table in BigQuery (choose its name) and Front Contacts. Use the record-matching and field-ownership rules from your mapping worksheet.
- Resolve the organization relationship and any owner or consent references required by the destination.
- Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.
Expected result: Test an email change, two records sharing an email, and a person associated with multiple organizations.
If it fails: Hold ambiguous matches for review and reconcile the person ID before retrying; preserve the consent decision already recorded by its owner.
Company reporting workflow
Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.
Starting event: A change to the selected Proposed company table in BigQuery (choose its name) or Accounts record needs a defined result in the other system.
- Start with BigQuery Proposed company table in BigQuery (choose its name) and Front Accounts. Use the record-matching and field-ownership rules from your mapping worksheet.
- Resolve parent organizations, business units, and currency references before dependent transactions.
- Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.
Expected result: Use two organizations with similar names and one with multiple business units. Verify that an update reaches the intended entity only.
If it fails: Repair the cross-system ID relationship before retrying dependent records; do not merge companies solely to remove a sync error.
Define an explicit handoff between BigQuery and Front
Validate the selected objects and operations even where connector-level direction is documented.
Starting event: A business event involving a selected business dataset needs a defined response involving Accounts or Contacts.
- Document what the source event means and which destination record or action should respond. A similar name does not establish a shared entity.
- Retain separate IDs and choose whether the destination is a report, a new work item, or a change to an existing record.
- Assign an approval owner and a duplicate-detection rule before running an action. Use a custom workflow only after its endpoint support is confirmed.
Expected result: An example input has an unambiguous destination and expected result; repeated delivery produces only the intended change.
If it fails: Resolve missing identity or ambiguous business meaning before retrying; route unsupported operations to the implementation owner.
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.
Proposed message or conversation table in BigQuery (choose its name) / Conversations
Test case
Test a delivery-state change, repeated message, private conversation, and reply linked to the correct thread.
Expected result
The expected message or conversation relationship is preserved with no duplicate action or unintended write.
Proposed contact table in BigQuery (choose its name) / Contacts
Test case
Test an email change, two records sharing an email, and a person associated with multiple organizations.
Expected result
The expected contact relationship is preserved with no duplicate action or unintended write.
Proposed company table in BigQuery (choose its name) / Accounts
Test case
Use two organizations with similar names and one with multiple business units. Verify that an update reaches the intended entity only.
Expected result
The expected company relationship is preserved with no duplicate action or unintended write.
Proposed application user or identity table in BigQuery (choose its name) / Teammates
Test case
Test a renamed login, disabled account, missing group, and a user existing in two tenants.
Expected result
The expected application user or identity relationship is preserved with no duplicate action or unintended write.
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.
Freshness and reconciliation
Test case
Measure source and destination times for the selected records under normal load and a burst. Reconcile IDs and values using the same filters and cutoff.
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.
Rejected or repeated message or conversation change
Investigate
Inspect BigQuery Proposed message or conversation table in BigQuery (choose its name) and Front Conversations, their IDs, and the destination error.
Next action
Check provider delivery state before retrying a send; replaying history must not send the message again.
Rejected or repeated contact change
Investigate
Inspect BigQuery Proposed contact table in BigQuery (choose its name) and Front Contacts, their IDs, and the destination error.
Next action
Hold ambiguous matches for review and reconcile the person ID before retrying; preserve the consent decision already recorded by its owner.
Rejected or repeated company change
Investigate
Inspect BigQuery Proposed company table in BigQuery (choose its name) and Front Accounts, their IDs, and the destination error.
Next action
Repair the cross-system ID relationship before retrying dependent records; do not merge companies solely to remove a sync error.
A record type or update is unavailable
Investigate
Check the BigQuery and Front 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 Front
See how each system detects changes and which updates the other system can receive. Each direction has its own permissions and record requirements.
BigQuery Front 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 Front. Its permissions and validation rules still apply; rejected records can be inspected in the Issues dashboard.
Front 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.
FAQ
BigQuery and Front integration FAQ
-
Stacksync connects BigQuery and Front 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. Create a Front connection in Stacksync and complete the authorization flow. 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.
-
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.
-
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.
-
Start by reviewing Proposed message or conversation table in BigQuery (choose its name) in BigQuery and Conversations in Front. Check how these records relate in your workflow, then confirm the actual fields and supported operations. Test record matching and one failed or repeated update before adding more records.
-
Use BigQuery Proposed message or conversation table in BigQuery (choose its name) and Front Conversations for the first pilot. Verify its identity and permitted direction, then add dependent records only when the acceptance checks pass.
Integration directory
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Next step
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