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 Campfire integration — two-way sync
Keep supported BigQuery and Campfire 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
Customer invoice 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 customer invoice table in BigQuery or Campfire Invoice needs a defined result in the other system.
Start with the proposed customer invoice table in BigQuery and Campfire Invoice. Use the record-matching and field-ownership rules from your mapping worksheet.
Resolve the customer or supplier, account codes, tax, currency, and accounting period before posting.
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 posted invoice, a partial payment, tax rounding, and a closed accounting period. Compare financial totals within the same entity and currency.
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 | Campfire record | Record matching | Field ownership |
|---|---|---|---|
| Proposed customer invoice tableProposed table; choose its name and schema.Reporting dataset | InvoiceDocumented record: Incremental: ; historical: ; delete detection: | Retain the invoice ID, issuer/legal entity, and original order reference; invoice numbers alone may overlap. | The financial system owns posting and accounting treatment. A posted invoice may need a credit or adjustment process instead of an overwrite. |
| Proposed supplier bill tableProposed table; choose its name and schema.Reporting dataset | BillDocumented record: Incremental: ; historical: ; delete detection: Every 24h | Retain the supplier-bill ID, supplier, legal entity, and document reference; distinguish it from a customer invoice. | Accounts payable owns approval and posting; a supplier bill is not an accounts-receivable invoice. |
| Proposed supplier or vendor tableProposed table; choose its name and schema.Reporting dataset | VendorDocumented record: Incremental: ; historical: ; delete detection: | Use the supplier ID within its business entity; a supplier name may occur in several subsidiaries. | Assign responsibility for approved supplier details; restrict changes to payment instructions to the business approval process. |
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 Campfire
Put your BigQuery and Campfire 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 Campfire workflow to test, define what success looks like, and decide who handles failed updates.
Join Campfire Invoices to marketing and product data already in BigQuery Projects for margin analysis.
Reconcile Bank Transactions against Invoices with SQL in BigQuery instead of manual exports.
Build board reporting on partitioned Tables that stay current with Campfire records.
What records can you sync?
Start with the records your workflow needs. Check each system’s read and write requirements before mapping fields.
Campfire
Read and write support varies by record
Record types covered in the setup guide
| Record types | Coverage and requirements |
|---|---|
| Incremental: ✅; historical: ✅; delete detection: ✅. Confirm field permissions and sync direction. |
| Incremental: ✅; historical: ✅; delete detection: ✅ Every 24h. Confirm field permissions and sync direction. |
| Incremental: ✅; historical: ✅; delete detection: N/A (cannot be deleted). 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 | Campfire |
|---|---|---|
| Integration interface | GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs | HTTP endpoints for bot integrations on a self-hosted instance |
| Authentication | Dedicated Google Cloud service account and JSON key | API key |
| Change detection | The setup provisions notification services using Eventarc and Cloud Run in the customer Google Cloud project. | Historical backfill and incremental change tracking are documented. |
| 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
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- SOC 2 Type II
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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.
Campfire
- Integration interface
- HTTP endpoints for bot integrations on a self-hosted instance
- Authentication
- API key — create an API user with a Super User Role in Campfire (Settings -> API Keys), generate an API Key secret, and provide it in the Stacksync connection setup
- Change detection
- Historical backfill and incremental change tracking are documented. Delete detection varies by object.
- Read access
- Available for supported records
- Write access
- Available for supported records
Setup requirements
- Create an API user with a Super User role in Campfire Settings > API Keys and generate its secret.
- Review the supported-object table for incremental sync and deletion behavior before selecting entities.
Limitations to check
- Incremental sync and delete detection vary by object; some deletions are checked only every 24 hours.
Technical documentation
Documentation reviewed 2026-09-15. Check the linked guides for current account and record requirements.
Prepare BigQuery and Campfire 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
Campfire setup checklist
- Create an API user with a Super User role in Campfire Settings > API Keys and generate its secret.
- Review the supported-object table for incremental sync and deletion behavior before selecting entities.
Setup guides: Authorize Campfire Connection
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 Campfire 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 customer invoice table in BigQuery (choose its name) / Invoice
Plan a customer invoice 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
- Campfire
- Documented record · Incremental: ✅; historical: ✅; delete detection: ✅
- Record identity
- Retain the invoice ID, issuer/legal entity, and original order reference; invoice numbers alone may overlap.
- Field ownership
- The financial system owns posting and accounting treatment. A posted invoice may need a credit or adjustment process instead of an overwrite.
Fields to include
- Source invoice ID
- Customer or supplier reference
- Line totals
- Currency
- Posting/payment status
- Record dependencies
- Resolve the customer or supplier, account codes, tax, currency, and accounting period before posting.
- Validation
- Test a posted invoice, a partial payment, tax rounding, and a closed accounting period. Compare financial totals within the same entity and currency.
- Recovery
- Determine whether a transaction posted before retrying. Follow the approved adjustment process for posted records.
References: Campfire: Invoice documentation
Reporting dataset
Proposed supplier bill table in BigQuery (choose its name) / Bill
Plan a supplier bill 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
- Campfire
- Documented record · Incremental: ✅; historical: ✅; delete detection: ✅ Every 24h
- Record identity
- Retain the supplier-bill ID, supplier, legal entity, and document reference; distinguish it from a customer invoice.
- Field ownership
- Accounts payable owns approval and posting; a supplier bill is not an accounts-receivable invoice.
Fields to include
- Source bill ID
- Supplier reference
- Entity
- Line amounts and currency
- Approval/posting state
- Record dependencies
- Resolve supplier, expense/account codes, entity, currency, and accounting period before bill lines.
- Validation
- Test a duplicate supplier document number in another entity, a partially paid bill, and a closed period.
- Recovery
- Verify whether the bill was approved or posted before retrying; use the approved adjustment process for posted bills.
References: Campfire: Bill documentation
Reporting dataset
Proposed supplier or vendor table in BigQuery (choose its name) / Vendor
Plan a supplier or vendor 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
- Campfire
- Documented record · Incremental: ✅; historical: ✅; delete detection: ✅
- Record identity
- Use the supplier ID within its business entity; a supplier name may occur in several subsidiaries.
- Field ownership
- Assign responsibility for approved supplier details; restrict changes to payment instructions to the business approval process.
Fields to include
- Source supplier ID
- Legal name
- Entity reference
- Payment terms
- Active status
- Record dependencies
- Resolve business-unit and payment-term references before purchasing transactions.
- Validation
- Test a supplier shared across subsidiaries and an inactive supplier referenced by an open bill.
- Recovery
- Review rejected supplier changes before retrying dependent bills; do not reactivate a supplier merely to make a write pass.
References: Campfire: Vendor documentation
Reporting dataset
Proposed journal or ledger entry table in BigQuery (choose its name) / Journal Entry
Plan a journal or ledger entry 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
- Campfire
- Documented record · Incremental: ✅; historical: ✅; delete detection: ✅
- Record identity
- Retain journal ID and line IDs within the legal entity and accounting period.
- Field ownership
- The ledger owns posting approval and period controls; an integration should not treat a posted journal as an ordinary editable row.
Fields to include
- Source journal ID
- Account references
- Debit/credit amounts
- Entity
- Accounting period
- Record dependencies
- Resolve chart-of-account, currency, entity, and dimension references before lines.
- Validation
- Verify balanced debits and credits, dimension requirements, and rejection for a closed period.
- Recovery
- Reconcile posting status and use the finance-approved reversal or adjustment process instead of replaying a posted entry.
References: Campfire: Journal Entry documentation
Reporting dataset
Proposed product or catalog item table in BigQuery (choose its name) / Product
Plan a product or catalog item 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
- Campfire
- Documented record · Incremental: ✅; historical: ✅; delete detection: ✅
- Record identity
- Distinguish the product ID, variant ID, SKU, and price-list entry; they are not interchangeable keys.
- Field ownership
- Assign ownership for catalog content, price, and stock separately.
Fields to include
- Source product ID
- SKU or variant reference
- Description
- Unit of measure
- Price-list reference
- Record dependencies
- Resolve units, variants, categories, and applicable price lists before order lines.
- Validation
- Test two variants of one product, a changed SKU, and a price that applies to only one market or currency.
- Recovery
- Repair the variant or price-list reference before retrying affected order lines; preserve existing transaction prices.
References: Campfire: Product documentation
Reporting dataset
Proposed credit note or credit memo table in BigQuery (choose its name) / Credit Memo
Plan a credit note or credit memo 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
- Campfire
- Documented record · Incremental: ✅; historical: ✅; delete detection: ✅
- Record identity
- Keep a distinct credit-document ID and its relationship to the original invoice or bill; it is not the original invoice itself.
- Field ownership
- Finance owns the reason, approval, and accounting treatment of the credit.
Fields to include
- Source credit-document ID
- Original document reference
- Customer/supplier reference
- Credit amount and currency
- Posting state
- Record dependencies
- Resolve the original invoice/bill, customer or supplier, legal entity, and account references.
- Validation
- Test a partial credit, several credits against one invoice, and a credit without a valid original-document relationship.
- Recovery
- Look up existing credits and posting state before retrying to prevent duplicated adjustments.
References: Campfire: Credit Memo documentation
Compare integration approaches
Use BigQuery Proposed customer invoice table in BigQuery (choose its name) and Campfire Invoice 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 the invoice ID, issuer/legal entity, and original order reference; invoice numbers alone may overlap. 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 Campfire 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 customer invoice table in BigQuery (choose its name) / Invoice, update direction, account tier, and related-record handling.
Custom API or workflow
- Best fit
- Consider when BigQuery and Campfire 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. Separate reading history from actions that send messages, grant access, or post transactions.
File or scheduled snapshot
- Best fit
- Consider for a one-time BigQuery / Campfire 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
Customer invoice 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 customer invoice table in BigQuery (choose its name) or Invoice record needs a defined result in the other system.
- Start with BigQuery Proposed customer invoice table in BigQuery (choose its name) and Campfire Invoice. Use the record-matching and field-ownership rules from your mapping worksheet.
- Resolve the customer or supplier, account codes, tax, currency, and accounting period before posting.
- 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 posted invoice, a partial payment, tax rounding, and a closed accounting period. Compare financial totals within the same entity and currency.
If it fails: Determine whether a transaction posted before retrying. Follow the approved adjustment process for posted records.
Supplier bill 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 supplier bill table in BigQuery (choose its name) or Bill record needs a defined result in the other system.
- Start with BigQuery Proposed supplier bill table in BigQuery (choose its name) and Campfire Bill. Use the record-matching and field-ownership rules from your mapping worksheet.
- Resolve supplier, expense/account codes, entity, currency, and accounting period before bill lines.
- 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 duplicate supplier document number in another entity, a partially paid bill, and a closed period.
If it fails: Verify whether the bill was approved or posted before retrying; use the approved adjustment process for posted bills.
Supplier or vendor 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 supplier or vendor table in BigQuery (choose its name) or Vendor record needs a defined result in the other system.
- Start with BigQuery Proposed supplier or vendor table in BigQuery (choose its name) and Campfire Vendor. Use the record-matching and field-ownership rules from your mapping worksheet.
- Resolve business-unit and payment-term references before purchasing 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: Test a supplier shared across subsidiaries and an inactive supplier referenced by an open bill.
If it fails: Review rejected supplier changes before retrying dependent bills; do not reactivate a supplier merely to make a write pass.
Reconcile Campfire business records with BigQuery
Validate the selected objects and operations even where connector-level direction is documented.
Starting event: A finance-owned record in Campfire needs operational visibility through a selected destination dataset.
- Select Bill or Invoice with the correct legal entity, period, and currency.
- Define a reporting relationship in BigQuery; do not equate customer records, ledger accounts, and posted transactions.
- Decide whether the process only reports a financial state or requests an approved accounting action, and maintain a separate transaction ID for each action.
Expected result: Totals reconcile within the same entity/currency/window; a repeated handoff creates no duplicate financial transaction.
If it fails: Verify posting and settlement state before retrying. Use the approved adjustment path for already-posted transactions.
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 customer invoice table in BigQuery (choose its name) / Invoice
Test case
Test a posted invoice, a partial payment, tax rounding, and a closed accounting period. Compare financial totals within the same entity and currency.
Expected result
The expected customer invoice relationship is preserved with no duplicate action or unintended write.
Proposed supplier bill table in BigQuery (choose its name) / Bill
Test case
Test a duplicate supplier document number in another entity, a partially paid bill, and a closed period.
Expected result
The expected supplier bill relationship is preserved with no duplicate action or unintended write.
Proposed supplier or vendor table in BigQuery (choose its name) / Vendor
Test case
Test a supplier shared across subsidiaries and an inactive supplier referenced by an open bill.
Expected result
The expected supplier or vendor relationship is preserved with no duplicate action or unintended write.
Proposed journal or ledger entry table in BigQuery (choose its name) / Journal Entry
Test case
Verify balanced debits and credits, dimension requirements, and rejection for a closed period.
Expected result
The expected journal or ledger entry 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 customer invoice change
Investigate
Inspect BigQuery Proposed customer invoice table in BigQuery (choose its name) and Campfire Invoice, their IDs, and the destination error.
Next action
Determine whether a transaction posted before retrying. Follow the approved adjustment process for posted records.
Rejected or repeated supplier bill change
Investigate
Inspect BigQuery Proposed supplier bill table in BigQuery (choose its name) and Campfire Bill, their IDs, and the destination error.
Next action
Verify whether the bill was approved or posted before retrying; use the approved adjustment process for posted bills.
Rejected or repeated supplier or vendor change
Investigate
Inspect BigQuery Proposed supplier or vendor table in BigQuery (choose its name) and Campfire Vendor, their IDs, and the destination error.
Next action
Review rejected supplier changes before retrying dependent bills; do not reactivate a supplier merely to make a write pass.
A record type or update is unavailable
Investigate
Check the BigQuery and Campfire 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 Campfire
See how each system detects changes and which updates the other system can receive. Each direction has its own permissions and record requirements.
BigQuery Campfire 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 Campfire. Its permissions and validation rules still apply; rejected records can be inspected in the Issues dashboard.
Campfire BigQuery Timing depends on the connected systems
Detect changesHistorical backfill and incremental change tracking are documented. Delete detection varies by object.
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.
- Incremental sync and delete detection vary by object; some deletions are checked only every 24 hours.
FAQ
BigQuery and Campfire integration FAQ
-
Stacksync connects BigQuery and Campfire 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 an API user with a Super User role in Campfire Settings > API Keys and generate its secret. 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.
-
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 customer invoice table in BigQuery (choose its name) in BigQuery and Invoice in Campfire. 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 customer invoice table in BigQuery (choose its name) and Campfire Invoice 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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