Can Amazon Aurora and MongoDB sync in both directions?

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.

What should be ready before the Amazon Aurora and MongoDB initial load?

Prepare both accounts, the selected object schemas, stable source and destination IDs, and the expected outcome. Identify the Amazon Aurora account, edition, environment, and business objects the integration must access. Use auto-generated ObjectId values for collection _id keys and run the database as a replica set. Use the pair worksheet to record ownership and acceptance criteria.

How fast does the Amazon Aurora to MongoDB sync run?

Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.

Will existing duplicates merge automatically?

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.

How do I map Amazon Aurora fields to MongoDB?

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.

What should I do if a record fails to sync?

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.

What does a Amazon Aurora and MongoDB integration cost?

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.

Database

Amazon Aurora and MongoDB integration

Plan how Amazon Aurora and MongoDB 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

Amazon Aurora

MongoDB

Review the integration scope

Teams building with Stacksync

Plan the connection your business needs

Explore a Amazon Aurora and MongoDB integration with a Stacksync engineer. Stacksync support for Amazon Aurora 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.

01 / Business process

Start with one meaningful update

Identify the record that changes in Amazon Aurora or MongoDB, where it needs to appear, and which team depends on it.

02 / Data access

Establish the available connection

Bring the objects, account editions, and required directions. An engineer can review the connector path, permissions, and field access.

03 / Success criteria

Define a result you can verify

Agree on record matching, acceptable delay, expected volume, and how your team will resolve failed updates.

Explore the technical references

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 AuroraConnection and object support require review

Record types to review with Stacksync

Record type	Coverage and requirements
Databases	Confirm support for this record type and the direction you need.
Schemas	Confirm support for this record type and the direction you need.
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.
Columns and Data Types	Confirm support for this record type and the direction you need.

Discuss Amazon Aurora requirements

MongoDBRead and write support varies by recordCollections

Record types covered in the setup guide

Record type	Coverage and requirements
Collections	See connector requirements. Confirm field permissions and sync direction.

Read the MongoDB connector guide

Connection requirements and limits

Amazon Aurora

Integration interface: MySQL or PostgreSQL wire protocol (SQL); optional RDS Data API over HTTPS
Authentication: Confirm the credentials, API plan, and permissions required for Amazon Aurora.
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 Aurora and the record types your workflow needs.
Review write-back, deletion handling, update timing, and account limits with the integration team.

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

MongoDB

Documentation reviewed 2026-09-15. Check the linked guides for current account and record requirements.

MongoDB setup guide

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 worksheet

CSV · No email required · Record matching, ownership, and test cases

Choose the data relationship

Choose the tables you need in Amazon Aurora and MongoDB, 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 Aurora source record should appear or trigger work in MongoDB. 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 Aurora and MongoDB 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 Aurora and MongoDB 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 Aurora / MongoDB 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 Aurora and MongoDB

Starting event: A selected source table needs an operational replica, a reporting projection, or a migration copy.

Choose actual tables in Amazon Aurora and MongoDB 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 Aurora and MongoDB 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 Aurora and MongoDB 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 Aurora and MongoDB

See how each system detects changes and which updates the other system can receive. Each direction has its own permissions and record requirements.

Amazon Aurora 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.

MongoDB Amazon Aurora 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 Amazon Aurora.

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 Amazon Aurora and MongoDB 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 Aurora setup checklist

Identify the Amazon Aurora account, edition, environment, and business objects the integration must access.
Confirm a Stacksync connector or implementation path for Amazon Aurora, including read/write support, authentication, and initial-load limits.

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.

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 Aurora and MongoDB 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

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.

Explore security controls

FAQ