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AWS Aurora PostgreSQL and Campfire integration

Plan how AWS Aurora PostgreSQL and Campfire should share data across your business. Work with Stacksync engineers on record mapping, system access, and the requirements for running the integration.

  • Scope your workflow with an integration engineer
  • Review the systems, records, and updates you need
Integration planning
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AWS Aurora PostgreSQL
Campfire
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Proposed workflow

Customer invoice reporting workflow

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Starting eventA change involving the proposed customer invoice table in AWS Aurora PostgreSQL or Campfire Invoice needs a defined result in the other system.

  1. Start with the proposed customer invoice table in AWS Aurora PostgreSQL and Campfire Invoice. Use the record-matching and field-ownership rules from your mapping worksheet.

  2. Resolve the customer or supplier, account codes, tax, currency, and accounting period before posting.

  3. 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 ownership

Proposed record relationships

Records to connect

Use these examples to define record matching and field ownership for your technical review.

Download the mapping worksheet

CSV · No email required

Example record relationships between AWS Aurora PostgreSQL and Campfire
AWS Aurora PostgreSQL recordCampfire recordRecord matchingField ownership
Proposed customer invoice tableProposed table; choose its name and schema.Reporting datasetInvoiceDocumented 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 datasetBillDocumented record: Incremental: ; historical: ; delete detection: Every 24hRetain 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 datasetVendorDocumented 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.

These relationships do not establish connector availability. Review the required connection and record operations with Stacksync.

Record coverage to review

Use documented coverage where available. Catalog record types are starting points for review and do not confirm Stacksync support.

AWS Aurora PostgreSQL

Connection and object support require review

Record types to review with Stacksync

Record typesCoverage and requirements
  • Tables
  • Databases and schemas
  • Rows
  • Columns
  • Primary keys and constraints
  • Views and materialized views
Confirm support for this record type and the direction you need.

Discuss AWS Aurora PostgreSQL requirements

Campfire

Read and write support varies by record

Record types covered in the setup guide

Record typesCoverage and requirements
  • Invoice
  • Debit Memo
  • Credit Memo
  • Bank Account
  • Bank Transaction
  • Journal Entry
  • Intercompany Journal Entry
  • Chart of Accounts
  • Chart Transaction
  • Fixed Asset
  • Fixed Asset Class
  • Budget
  • Transaction Match
  • Vendor
  • Tag
  • Cost Allocation
  • Custom Field
  • File
  • Contract
  • Contract Customer
  • Product
  • Product Bundle
  • Revenue Transaction
  • Customer Currency
  • Reconciliation Report
  • Webhook
Incremental: ✅; historical: ✅; delete detection: ✅. Confirm field permissions and sync direction.
  • Bill
  • Fixed Asset Automation Rule
  • Vendor Custom Field
  • Department
  • Entity
  • Tag Group
Incremental: ✅; historical: ✅; delete detection: ✅ Every 24h. Confirm field permissions and sync direction.
  • Fixed Asset Automation Match
  • Vendor Summary
Incremental: ✅; historical: ✅; delete detection: N/A (cannot be deleted). Confirm field permissions and sync direction.

Read the Campfire connector guide

Connection essentials

Confirm Stacksync support and account requirements for undocumented connections. Interface information alone does not establish connector availability.

View setup requirements and limits
Connection requirementAWS Aurora PostgreSQLCampfire
Integration interfaceSQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBCHTTP endpoints for bot integrations on a self-hosted instance
AuthenticationConfirm the credentials, API plan, and permissions required for AWS Aurora PostgreSQL.API key
Change detectionConfirm how Stacksync detects changes for this connector and the objects you need.Historical backfill and incremental change tracking are documented.
Read accessConfirm with StacksyncAvailable for supported records
Write accessConfirm with StacksyncAvailable for supported records

Enterprise controls

Security and control for your integrations

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Compliance and data transfers

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  • 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 guide

Implementation

Technical reference

Review setup, record relationships, testing, and recovery for your implementation.

Authentication, permissions and API limits

Connection requirements and limits

AWS Aurora PostgreSQL
Integration interface
SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC
Authentication
Confirm the credentials, API plan, and permissions required for AWS Aurora PostgreSQL.
Change detection
Confirm how Stacksync detects changes for this connector and the objects you need.
Read access
Confirm with Stacksync
Write access
Confirm with Stacksync
Setup requirements
  • Identify the AWS Aurora PostgreSQL account, edition, environment, and business objects the integration must access.
  • Confirm a Stacksync connector or implementation path for AWS Aurora PostgreSQL, including read/write support, authentication, and initial-load limits.
Limitations to check
  • Confirm Stacksync support for AWS Aurora PostgreSQL and the record types your workflow needs.
  • Review write-back, deletion handling, update timing, and account limits with the integration team.
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.

Campfire setup guide

Prepare AWS Aurora PostgreSQL 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.

AWS Aurora PostgreSQL setup checklist
  • Identify the AWS Aurora PostgreSQL account, edition, environment, and business objects the integration must access.
  • Confirm a Stacksync connector or implementation path for AWS Aurora PostgreSQL, including read/write support, authentication, and initial-load limits.
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 AWS Aurora PostgreSQL and Campfire planning worksheet to capture these decisions. Record the access owner in the worksheet and enter credentials only in the connection setup.

Talk to an engineer · Review current pricing

Record identity and field ownership

Use these data-model references to describe the records your connection needs. They are planning examples; connector availability and supported operations must be established before implementation.

Download the mapping worksheet · CSV, no email required

Reporting dataset

Proposed customer invoice table in AWS Aurora PostgreSQL (choose its name) / Invoice

Plan a customer invoice dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

AWS Aurora PostgreSQL
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 AWS Aurora PostgreSQL (choose its name) / Bill

Plan a supplier bill dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

AWS Aurora PostgreSQL
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 AWS Aurora PostgreSQL (choose its name) / Vendor

Plan a supplier or vendor dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

AWS Aurora PostgreSQL
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 AWS Aurora PostgreSQL (choose its name) / Journal Entry

Plan a journal or ledger entry dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

AWS Aurora PostgreSQL
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 AWS Aurora PostgreSQL (choose its name) / Product

Plan a product or catalog item dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

AWS Aurora PostgreSQL
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 AWS Aurora PostgreSQL (choose its name) / Credit Memo

Plan a credit note or credit memo dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

AWS Aurora PostgreSQL
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

Choose a method around one example record and the update your business needs. Use Proposed customer invoice table in AWS Aurora PostgreSQL (choose its name) / Invoice to review record matching and confirm Stacksync support for the required operations. Compare ongoing sync, a custom workflow, and a scheduled export against that requirement.

Stacksync managed sync

Best fit
Review compatibility with a Stacksync engineer using an example of the records and updates you need.
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 AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL (choose its name) / Invoice, update direction, account tier, and related-record handling.

Custom API or workflow

Best fit
Consider when AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL / 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

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Starting event: A change to the selected Proposed customer invoice table in AWS Aurora PostgreSQL (choose its name) or Invoice record needs a defined result in the other system.

  1. Start with AWS Aurora PostgreSQL Proposed customer invoice table in AWS Aurora PostgreSQL (choose its name) and Campfire Invoice. Use the record-matching and field-ownership rules from your mapping worksheet.
  2. Resolve the customer or supplier, account codes, tax, currency, and accounting period before posting.
  3. 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

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Starting event: A change to the selected Proposed supplier bill table in AWS Aurora PostgreSQL (choose its name) or Bill record needs a defined result in the other system.

  1. Start with AWS Aurora PostgreSQL Proposed supplier bill table in AWS Aurora PostgreSQL (choose its name) and Campfire Bill. Use the record-matching and field-ownership rules from your mapping worksheet.
  2. Resolve supplier, expense/account codes, entity, currency, and accounting period before bill lines.
  3. 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

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Starting event: A change to the selected Proposed supplier or vendor table in AWS Aurora PostgreSQL (choose its name) or Vendor record needs a defined result in the other system.

  1. Start with AWS Aurora PostgreSQL Proposed supplier or vendor table in AWS Aurora PostgreSQL (choose its name) and Campfire Vendor. Use the record-matching and field-ownership rules from your mapping worksheet.
  2. Resolve business-unit and payment-term references before purchasing transactions.
  3. 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 AWS Aurora PostgreSQL

This is an evaluation scenario; connector and operation support require confirmation.

Starting event: A finance-owned record in Campfire needs operational visibility through a selected destination dataset.

  1. Select Bill or Invoice with the correct legal entity, period, and currency.
  2. Define a reporting relationship in AWS Aurora PostgreSQL; do not equate customer records, ledger accounts, and posted transactions.
  3. 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 AWS Aurora PostgreSQL (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 AWS Aurora PostgreSQL (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 AWS Aurora PostgreSQL (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 AWS Aurora PostgreSQL (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

Bring an example source record and the intended destination operation to the compatibility review. Confirm the supported route before granting write access.

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 AWS Aurora PostgreSQL Proposed customer invoice table in AWS Aurora PostgreSQL (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 AWS Aurora PostgreSQL Proposed supplier bill table in AWS Aurora PostgreSQL (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 AWS Aurora PostgreSQL Proposed supplier or vendor table in AWS Aurora PostgreSQL (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 AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL 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.

AWS Aurora PostgreSQL Campfire Direction requires confirmation

Detect changesConfirm how Stacksync detects changes for this connector and the objects you need.

Apply updatesConfirm that Stacksync can create or update the records you need in Campfire.

Campfire AWS Aurora PostgreSQL Direction requires confirmation

Detect changesHistorical backfill and incremental change tracking are documented. Delete detection varies by object.

Apply updatesConfirm that Stacksync can create or update the records you need in AWS Aurora PostgreSQL.

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.
  • Review write-back, deletion handling, update timing, and account limits with the integration team.
  • Incremental sync and delete detection vary by object; some deletions are checked only every 24 hours.
FAQ

AWS Aurora PostgreSQL and Campfire integration FAQ

Further reading

Resources for your technical review

Next step

Plan your integration with an engineer

Walk through your AWS Aurora PostgreSQL and Campfire records, field mappings, and requirements with an integration engineer.

Talk to an engineer Explore plans and pricing