Start with one meaningful update
Identify the record that changes in MongoDB or Postgres Heroku, where it needs to appear, and which team depends on it.
Database
MongoDB and Postgres Heroku integration
Plan how MongoDB and Postgres Heroku 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
stacksync.com Your integration scope
Start with the update your team needs
Bring the source, destination, and record types. A Stacksync engineer will help establish the available implementation path.
Review the integration scopeBuild the right connection
Turn your integration requirement into a clear implementation path
Explore a MongoDB and Postgres Heroku integration with a Stacksync engineer. Stacksync support for Postgres Heroku 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.
Establish the available connection
Bring the objects, account editions, and required directions. An engineer can review the connector path, permissions, and field access.
Define a result you can verify
Agree on record matching, acceptable delay, expected volume, and how your team will resolve failed updates.
Adopted by fast-scaling companies moving mission-critical data in real time
Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Data and connection referencesAvailable documentation, candidate record relationships, and questions for your technical review.
Record coverage for your integration
Compare the record types in MongoDB and Postgres Heroku, then choose the ones your workflow needs. Check each record's read and write support before mapping fields between systems.
MongoDB
Record types covered in the setup guide
| Object or data type | Coverage and checks |
|---|---|
| Collections | See connector requirements. Confirm field permissions and sync direction. |
Postgres Heroku
Record types to review with Stacksync
| Object or data type | Coverage and checks |
|---|---|
| Tables | Confirm support for this record type and the direction you need. |
| Views | Confirm support for this record type and the direction you need. |
| Materialized Views | Confirm support for this record type and the direction you need. |
| Schemas | Confirm support for this record type and the direction you need. |
View 2 more record types
| Object or data type | Coverage and checks |
|---|---|
| Primary and Unique Keys | Confirm support for this record type and the direction you need. |
| JSONB Columns | Confirm support for this record type and the direction you need. |
Connection requirements and limits
MongoDB
- Integration interface
- MongoDB wire protocol via official drivers; Atlas additionally offers an administration REST API for cluster management
- Authentication
- Database credentials (username/password) or TLS/SSL X.509 certificate (.pem upload), entered individually or via a MongoDB connection string (SRV or standard); Stacksync IP allowlisting required
- Change detection
- Stacksync uses MongoDB oplog and change streams. A replica set is required, including for a single-node deployment.
- Read access
- Available for supported records
- Write access
- Available for supported records
Limitations to check
- Individual collection-only permission grants are insufficient for change replication; grant access at the database level.
- Only ObjectId collection keys are supported; SSH tunneling has protocol and deployment restrictions in the guide.
Technical documentation
Documentation reviewed 2026-09-15. Check the linked guides for current account and record requirements.
Postgres Heroku
- Integration interface
- SQL wire protocol (standard PostgreSQL)
- Authentication
- Confirm the credentials, API plan, and permissions required for Postgres Heroku.
- 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
Limitations to check
- Confirm Stacksync support for Postgres Heroku and the record types your workflow needs.
- Review write-back, deletion handling, update timing, and account limits with the integration team.
Prepare your technical review
Use the worksheets and reference checks to capture record identity, ownership, and the result your business expects.
Implementation reference
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 worksheetCSV · No email required · Record matching, ownership, and test cases
Choose the data relationship
Choose the tables you need in MongoDB and Postgres Heroku, then map their keys, field types, and filters. Your schemas determine how records relate and which system should own each field. Use the worksheet to document those choices before testing the first load.
Architecture decision
Choose how to connect your systems
Choose a method around one example record and the update your business needs. Define how the MongoDB source record should appear or trigger work in Postgres Heroku. 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
- Provide your table or object schemas and one example update; confirm the supported keys, fields, and direction.
Native vendor integration
- Best fit
- A vendor-built integration may fit if it supports your MongoDB and Postgres Heroku record types.
- Operating responsibility
- Can reduce setup for a supported workflow. You may need another method for records or business steps it does not cover.
- Before you choose
- First check whether either vendor offers this integration. If available, verify your record types, update direction, account tier, and related-record handling.
Custom API or workflow
- Best fit
- Consider when MongoDB and Postgres Heroku 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 MongoDB / Postgres Heroku 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 reference
From a business event to the right update
Open a workflow to see its trigger, record relationships, and expected result.
Plan table mappings between MongoDB and Postgres Heroku
Starting event: A selected source table needs an operational replica, a reporting projection, or a migration copy.
- Choose actual tables in MongoDB and Postgres Heroku and compare their schemas and record types.
- Specify the primary/business key, type conversions, filter boundaries, relationship dependencies, and the source of each writable field.
- Choose a one-way copy or two-way sync according to connector support and your update rules. Keep source views, aggregate outputs, and writable base tables distinct.
Expected result: Counts reconcile within identical filters; updates preserve keys; precision, nulls, deletes, and schema changes follow the agreed mapping rules.
If it fails: Compare current source state, key mapping, and destination constraints before retrying. Reconcile the backlog after any schema or permission change.
Production readiness
Test the behavior your business depends on
Keep both record IDs with the expected and actual result. Reconcile the same filters and time window in each system.
Direction and permissions
Test case
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.
Schema, keys, and reconciliation
Test case
Use an actual MongoDB and Postgres Heroku table. Compare key uniqueness, nulls, decimal precision, timezone conversions, and counts within identical filters.
Expected result
The process meets its agreed freshness target and reconciliation has no unexplained differences.
Failure recovery
Find the cause. Restore the data flow.
Start with the failed record and the destination error, then inspect the source value, field requirements, and access.
A record type or update is unavailable
Investigate
Check the MongoDB and Postgres Heroku 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.
How updates move between MongoDB and Postgres Heroku
See how each system detects changes and which updates the other system can receive. Each direction has its own permissions and record requirements.
MongoDB Postgres Heroku Direction requires confirmation
Detect changesStacksync uses MongoDB oplog and change streams. A replica set is required, including for a single-node deployment.
Apply updatesConfirm that Stacksync can create or update the records you need in Postgres Heroku.
Postgres Heroku MongoDB 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 MongoDB.
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.
Prepare MongoDB and Postgres Heroku 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.
MongoDB setup checklist
- Use auto-generated ObjectId values for collection _id keys and run the database as a replica set.
- Allow Stacksync network access and grant database-level permissions to read/write collections and access the oplog.
Postgres Heroku setup checklist
- Identify the Postgres Heroku account, edition, environment, and business objects the integration must access.
- Confirm a Stacksync connector or implementation path for Postgres Heroku, 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 MongoDB and Postgres Heroku planning worksheet to capture these decisions. Record the access owner in the worksheet and enter credentials only in the connection setup.
FAQ
MongoDB and Postgres Heroku integration FAQ
-
Explore a MongoDB and Postgres Heroku integration with a Stacksync engineer. Stacksync support for Postgres Heroku 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.
-
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. Use auto-generated ObjectId values for collection _id keys and run the database as a replica set. Identify the Postgres Heroku account, edition, environment, and business objects the integration must access. Use the pair worksheet to record ownership and acceptance criteria.
-
Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.
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No. Stacksync documents that pre-existing duplicates are not merged automatically when two-way sync begins. Review the initial dataset and matching plan before enabling it; an empty destination can simplify the first load.
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Start with one business entity and a stable record ID. Map a small set of editable fields with compatible types, test required values and relationships, then expand after the pilot passes.
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Check the destination error, field constraints, permissions, and current source value. The Stacksync issues dashboard supports retry and revert; retry reads current source values, so verify the intended record state before acting.
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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.
-
Choose the tables you need in MongoDB and Postgres Heroku, then map their keys, field types, and filters. Your schemas determine how records relate and which system should own each field. Use the worksheet to document those choices before testing the first load.
-
Choose a method around one example record and the update your business needs. Define how the MongoDB source record should appear or trigger work in Postgres Heroku. Compare ongoing sync, a custom workflow, and a scheduled export against that requirement.
Related integration plans
Explore another route involving one of these systems.
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Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
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