Start with one meaningful update
Identify the record that changes in Amazon DynamoDB or Postgres Heroku, where it needs to appear, and which team depends on it.
Database
Amazon DynamoDB and Postgres Heroku integration
Plan how Amazon DynamoDB 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
Teams building with Stacksync
Plan the connection your business needs
Explore a Amazon DynamoDB and Postgres Heroku integration with a Stacksync engineer. Stacksync support for Amazon DynamoDB and 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.
Technical referenceAvailable documentation, candidate record relationships, and questions for your technical review.
What records can you sync?
Explore the record types and read/write requirements for each system.
Amazon DynamoDBConnection and object support require review
Record types to review with Stacksync
| Record type | Coverage and requirements |
|---|---|
| Tables | Confirm support for this record type and the direction you need. |
| Items | Confirm support for this record type and the direction you need. |
| Attributes | Confirm support for this record type and the direction you need. |
| Partition and Sort Keys | Confirm support for this record type and the direction you need. |
| Global Secondary Indexes | Confirm support for this record type and the direction you need. |
| DynamoDB Streams | Confirm support for this record type and the direction you need. |
Postgres HerokuConnection and object support require review
Record types to review with Stacksync
| Record type | Coverage and requirements |
|---|---|
| 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. |
| 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
Amazon DynamoDB
- Integration interface
- Proprietary JSON-over-HTTPS API accessed through AWS SDKs; PartiQL supported for SQL-like queries
- Authentication
- Confirm the credentials, API plan, and permissions required for Amazon DynamoDB.
- 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 Amazon DynamoDB and the record types your workflow needs.
- Review write-back, deletion handling, update timing, and account limits with the integration team.
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 Amazon DynamoDB 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 Amazon DynamoDB 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 Amazon DynamoDB 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 Amazon DynamoDB 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 Amazon DynamoDB / 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 Amazon DynamoDB and Postgres Heroku
Starting event: A selected source table needs an operational replica, a reporting projection, or a migration copy.
- Choose actual tables in Amazon DynamoDB 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 Amazon DynamoDB 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 Amazon DynamoDB 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 Amazon DynamoDB 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.
Amazon DynamoDB Postgres Heroku 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 Postgres Heroku.
Postgres Heroku Amazon DynamoDB 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 Amazon DynamoDB.
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.
Prepare Amazon DynamoDB 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.
Amazon DynamoDB setup checklist
- Identify the Amazon DynamoDB account, edition, environment, and business objects the integration must access.
- Confirm a Stacksync connector or implementation path for Amazon DynamoDB, including read/write support, authentication, and initial-load limits.
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 Amazon DynamoDB and Postgres Heroku planning worksheet to capture these decisions. Record the access owner in the worksheet and enter credentials only in the connection setup.
Security and control for your integrations
Use SSO and SCIM to manage access, secure connection options to reach your systems, and record-level retry and revert controls to resolve sync errors.
FAQ
Amazon DynamoDB and Postgres Heroku integration FAQ
-
Explore a Amazon DynamoDB and Postgres Heroku integration with a Stacksync engineer. Stacksync support for Amazon DynamoDB and 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. Identify the Amazon DynamoDB account, edition, environment, and business objects the integration must access. Identify the Postgres Heroku 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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Choose the tables you need in Amazon DynamoDB 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 Amazon DynamoDB source record should appear or trigger work in Postgres Heroku. Compare ongoing sync, a custom workflow, and a scheduled export against that requirement.
Find the right integration path
Walk through your Amazon DynamoDB and Postgres Heroku records, field mappings, and requirements with an integration engineer.
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