Google Cloud SQL
Connection and object support require review
Record types to review with Stacksync
| Record types | Coverage and requirements |
|---|---|
| Confirm support for this record type and the direction you need. |
Database / Human resources · Two-way sync platform
Google Cloud SQL and Greenhouse integration
Plan how Google Cloud SQL and Greenhouse 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
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Proposed workflow
Candidate or application reporting workflow
Planning example. Stacksync support for the required connection and record operations needs a technical review.
Starting eventA change involving the proposed candidate or application table in Google Cloud SQL or Greenhouse Candidates needs a defined result in the other system.
Start with the proposed candidate or application table in Google Cloud SQL and Greenhouse Candidates. Use the record-matching and field-ownership rules from your mapping worksheet.
Resolve job, application, recruiter, and any approved employee handoff.
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 multiple applications for one candidate and an application withdrawal after export.
Review records and field ownershipProposed record relationships
Records to connect
Use these examples to define record matching and field ownership for your technical review.
Download the mapping worksheetCSV · No email required
| Google Cloud SQL record | Greenhouse record | Record matching | Field ownership |
|---|---|---|---|
| Proposed candidate or application tableProposed table; choose its name and schema.Reporting dataset | CandidatesProposed record; confirm Stacksync object support. | Keep person/candidate and job-application IDs separate; one candidate may have several applications. | Recruiting owns stage transitions and applicant-data handling; an accepted offer does not itself establish an employee record. |
| Proposed application user or identity tableProposed table; choose its name and schema.Reporting dataset | UsersProposed record; confirm Stacksync object support. | Use the immutable user ID within the tenant or directory. Do not equate an application user with a CRM customer contact. | Identity and application owners approve account lifecycle and access changes; synchronize only approved attributes. |
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.
Greenhouse
Connection and object support require review
Record types to review with Stacksync
| Record types | Coverage and requirements |
|---|---|
| Confirm support for this record type and the direction you need. |
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 requirement | Google Cloud SQL | Greenhouse |
|---|---|---|
| Integration interface | Native SQL wire protocols (MySQL, PostgreSQL, SQL Server) plus a REST admin API for instance management | Harvest REST API (plus read-only Job Board API and the Ingestion API for bulk candidate import) |
| Authentication | Confirm the credentials, API plan, and permissions required for Google Cloud SQL. | Confirm the credentials, API plan, and permissions required for Greenhouse. |
| Change detection | Confirm how Stacksync detects changes for this connector and the objects you need. | Confirm how Stacksync detects changes for this connector and the objects you need. |
| Read access | Confirm with Stacksync | Confirm with Stacksync |
| Write access | Confirm with Stacksync | Confirm with Stacksync |
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
Google Cloud SQL
- Integration interface
- Native SQL wire protocols (MySQL, PostgreSQL, SQL Server) plus a REST admin API for instance management
- Authentication
- Confirm the credentials, API plan, and permissions required for Google Cloud SQL.
- 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 Google Cloud SQL account, edition, environment, and business objects the integration must access.
- Confirm a Stacksync connector or implementation path for Google Cloud SQL, including read/write support, authentication, and initial-load limits.
Limitations to check
- Confirm Stacksync support for Google Cloud SQL and the record types your workflow needs.
- Review write-back, deletion handling, update timing, and account limits with the integration team.
Greenhouse
- Integration interface
- Harvest REST API (plus read-only Job Board API and the Ingestion API for bulk candidate import)
- Authentication
- Confirm the credentials, API plan, and permissions required for Greenhouse.
- 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 Greenhouse account, edition, environment, and business objects the integration must access.
- Confirm a Stacksync connector or implementation path for Greenhouse, including read/write support, authentication, and initial-load limits.
Limitations to check
- Confirm Stacksync support for Greenhouse and the record types your workflow needs.
- Review write-back, deletion handling, update timing, and account limits with the integration team.
Technical documentation
Prepare Google Cloud SQL and Greenhouse 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.
Google Cloud SQL setup checklist
- Identify the Google Cloud SQL account, edition, environment, and business objects the integration must access.
- Confirm a Stacksync connector or implementation path for Google Cloud SQL, including read/write support, authentication, and initial-load limits.
Greenhouse setup checklist
- Identify the Greenhouse account, edition, environment, and business objects the integration must access.
- Confirm a Stacksync connector or implementation path for Greenhouse, including read/write support, authentication, and initial-load limits.
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 Google Cloud SQL and Greenhouse 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
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 candidate or application table in Google Cloud SQL (choose its name) / Candidates
Plan a candidate or application dataset while preserving its source meaning.
Planning example. Stacksync support for the required connection and record operations needs a technical review.
- Google Cloud SQL
- Your database schema
- Greenhouse
- Object support to establish
- Record identity
- Keep person/candidate and job-application IDs separate; one candidate may have several applications.
- Field ownership
- Recruiting owns stage transitions and applicant-data handling; an accepted offer does not itself establish an employee record.
Fields to include
- Source candidate ID
- Application ID
- Job reference
- Stage
- Consent or retention state
- Record dependencies
- Resolve job, application, recruiter, and any approved employee handoff.
- Validation
- Test multiple applications for one candidate and an application withdrawal after export.
- Recovery
- Repair application identity before retrying; preserve withdrawal and retention decisions.
References: Greenhouse: Candidates documentation
Reporting dataset
Proposed application user or identity table in Google Cloud SQL (choose its name) / Users
Plan a application user or identity dataset while preserving its source meaning.
Planning example. Stacksync support for the required connection and record operations needs a technical review.
- Google Cloud SQL
- Your database schema
- Greenhouse
- 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: Greenhouse: Users documentation
Compare integration approaches
Choose a method around one example record and the update your business needs. Use Proposed candidate or application table in Google Cloud SQL (choose its name) / Candidates 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: Keep person/candidate and job-application IDs separate; one candidate may have several applications. 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 Google Cloud SQL and Greenhouse 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 candidate or application table in Google Cloud SQL (choose its name) / Candidates, update direction, account tier, and related-record handling.
Custom API or workflow
- Best fit
- Consider when Google Cloud SQL and Greenhouse 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 Google Cloud SQL / Greenhouse 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
Candidate or application 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 candidate or application table in Google Cloud SQL (choose its name) or Candidates record needs a defined result in the other system.
- Start with Google Cloud SQL Proposed candidate or application table in Google Cloud SQL (choose its name) and Greenhouse Candidates. Use the record-matching and field-ownership rules from your mapping worksheet.
- Resolve job, application, recruiter, and any approved employee handoff.
- 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 multiple applications for one candidate and an application withdrawal after export.
If it fails: Repair application identity before retrying; preserve withdrawal and retention decisions.
Application user or identity 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 application user or identity table in Google Cloud SQL (choose its name) or Users record needs a defined result in the other system.
- Start with Google Cloud SQL Proposed application user or identity table in Google Cloud SQL (choose its name) and Greenhouse Users. Use the record-matching and field-ownership rules from your mapping worksheet.
- Resolve tenant and group references and establish a protected administrative-account policy.
- 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 renamed login, disabled account, missing group, and a user existing in two tenants.
If it fails: Review access impact before retrying a lifecycle change; reconcile current identity state and retain an approval trail.
Worker lifecycle planning for Greenhouse and Google Cloud SQL
This is an evaluation scenario; connector and operation support require confirmation.
Starting event: A worker joins, moves, or leaves in Greenhouse; a downstream process in Google Cloud SQL needs the approved context.
- Identify the worker and employment record represented by Candidates or Applications. Retain effective dates and organizational scope.
- Determine what a selected destination dataset represents: an operational task, a user identity, or a reporting row. Define a relationship; do not map these records as if they were the employee itself.
- Require the process owner to approve any access, payroll, or account action and assign an exception owner.
Expected result: Rehires and concurrent assignments keep distinct employment context; delayed updates do not reverse a newer effective state.
If it fails: Inspect employment identity and effective dates before retrying the downstream action.
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 candidate or application table in Google Cloud SQL (choose its name) / Candidates
Test case
Test multiple applications for one candidate and an application withdrawal after export.
Expected result
The expected candidate or application relationship is preserved with no duplicate action or unintended write.
Proposed application user or identity table in Google Cloud SQL (choose its name) / Users
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
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 candidate or application change
Investigate
Inspect Google Cloud SQL Proposed candidate or application table in Google Cloud SQL (choose its name) and Greenhouse Candidates, their IDs, and the destination error.
Next action
Repair application identity before retrying; preserve withdrawal and retention decisions.
Rejected or repeated application user or identity change
Investigate
Inspect Google Cloud SQL Proposed application user or identity table in Google Cloud SQL (choose its name) and Greenhouse Users, their IDs, and the destination error.
Next action
Review access impact before retrying a lifecycle change; reconcile current identity state and retain an approval trail.
A record type or update is unavailable
Investigate
Check the Google Cloud SQL and Greenhouse 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 Google Cloud SQL and Greenhouse
See how each system detects changes and which updates the other system can receive. Each direction has its own permissions and record requirements.
Google Cloud SQL Greenhouse 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 Greenhouse.
Greenhouse Google Cloud SQL 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 Google Cloud SQL.
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.
- Review write-back, deletion handling, update timing, and account limits with the integration team.
FAQ
Google Cloud SQL and Greenhouse integration FAQ
-
Explore a Google Cloud SQL and Greenhouse integration with a Stacksync engineer. Stacksync support for Google Cloud SQL and Greenhouse is not established by the connector documentation reviewed for this page. Start with one record and the update your business needs to identify an implementation path.
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Confirm two-way support with Stacksync for the records and fields you need in both systems. Access to a vendor API does not confirm that its Stacksync connector supports write-back.
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Prepare both accounts, the selected object schemas, stable source and destination IDs, and the expected outcome. Identify the Google Cloud SQL account, edition, environment, and business objects the integration must access. Identify the Greenhouse account, edition, environment, and business objects the integration must access. Use the pair worksheet to record ownership and acceptance criteria.
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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.
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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.
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Start by reviewing Proposed candidate or application table in Google Cloud SQL (choose its name) in Google Cloud SQL and Candidates in Greenhouse. 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.
-
Choose a method around one example record and the update your business needs. Use Proposed candidate or application table in Google Cloud SQL (choose its name) / Candidates 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.
Integration directory
Related integrations
Review documented support, sync direction, and setup requirements on each pair page. Search all 435 integrations listed for Google Cloud SQL and Greenhouse.
Next step
Plan your integration with an engineer
Walk through your Google Cloud SQL and Greenhouse records, field mappings, and requirements with an integration engineer.
