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Choose a migration route based on your source and constraints
Aurora has MySQL-compatible and PostgreSQL-compatible editions, and the available paths differ between them. AWS identifies native tools, snapshots in applicable cases, AWS Database Migration Service (AWS DMS), and replication among the options; physical migration may also be an option for some MySQL cases. Compatibility and database size affect which methods are viable.
Compare candidate methods against the source and target engine versions, database size, source location and network access, outage tolerance, schema-conversion work, and the time and people available for validation and rollback. The table is a shortlist, not a compatibility matrix: check AWS’s live service and engine-version documentation for the exact source, target, and Region before committing.
| Situation | Candidate route | What to check |
|---|---|---|
| MySQL-compatible source and a manageable export/import window | Logical export and import with native tools such as mysqldump |
Test throughput, object coverage, and the outage window using representative data. AWS lists native tools, but there is no universal migration-duration estimate. |
| Amazon RDS for MySQL source | RDS snapshot migration, where supported | Confirm source and target compatibility and the current snapshot feature constraints. |
| Large external MySQL source | Evaluate physical migration as well as logical methods | AWS says physical migration is faster than logical migration, especially for large databases. That general comparison does not establish suitability for every topology or configuration. |
| PostgreSQL source moving to Aurora PostgreSQL-Compatible | Native or third-party full load; AWS DMS full load with ongoing replication; or native full load followed by DMS replication | Choose according to the load window, ongoing replication needs, and operational constraints. |
| Source and target use different database engines | Assess and convert schema and code separately from data movement | Review objects that cannot be converted automatically and plan manual remediation. Schema conversion alone does not copy table data. |
| Application outage must be limited | Initial load plus ongoing change replication, followed by a rehearsed cutover | Set an acceptable replication-lag threshold and test consistency, application behavior, and fallback steps for your actual setup. |
Inventory the source and choose an Aurora edition
Before selecting a method, record the source engine and exact version, database size and growth, hosting location, write activity, connectivity restrictions, recovery needs, and the outage the application can tolerate. Also inventory extensions, schema objects, stored code, and application dependencies that might behave differently on the target.
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Choose Aurora MySQL-Compatible or Aurora PostgreSQL-Compatible according to application compatibility and the conversion effort you can support. Source engine compatibility and data size influence the available migration options, so do not choose a route based only on a generic description of “Aurora migration.”
Verify compatibility and prerequisites
Check current supported source and target versions, required privileges, network reachability, credentials, service constraints, and Region availability before scheduling a production move. Verify these against the live AWS documentation: engine support and service behavior can change.
Check the console auto-migration requirements
For the Aurora console auto-migration workflow described by AWS, create an equivalent target cluster first. The source and target must use the same engine and compatible versions. AWS says this console migration action reduces time and resources for source databases smaller than 1 TiB; that is a feature-specific threshold, not a cutoff that applies to other migration methods.
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In that console workflow, AWS distinguishes full-load modes from CDC. Its documentation says the target is unavailable to applications during migration in full-load modes, while CDC can keep the target available as changes replicate. This describes target availability in that workflow; check the precise behavior of the method and configuration you plan to use.
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AWS documents a specific example of a version restriction: MySQL 8.0.11, 8.0.13, and 8.0.15 cannot migrate to Aurora MySQL 3.05 and higher, and AWS recommends upgrading those source versions to MySQL 8.0.28 before migration. This example is not a complete compatibility matrix. Confirm the current supported combinations for your exact source and target versions.
Assess schema and application conversion separately
For a cross-engine migration, assess schema and application code before moving data. AWS DMS Schema Conversion can assess conversion complexity, convert supported schema and code objects, and flag work that needs manual conversion. It does not migrate data. As AWS puts it, “DMS Schema Conversion converts your database schema, not your data.” Plan a distinct data-transfer path as well as time to review and remediate conversion findings.
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AWS’s SQL Server-to-Aurora PostgreSQL-Compatible walkthrough covers conversion of tables, views, stored procedures, functions, data types, synonyms, and other objects. It marks non-convertible objects for manual work. Its stated three-hour duration is an estimate for completing that introductory exercise—not a database-migration estimate.
Select and rehearse the data-transfer method
For MySQL-compatible sources
AWS lists logical dump and import, RDS snapshot migration in applicable cases, AWS DMS, and replication among the MySQL migration options. Physical migration may be available in supported cases; AWS’s comparison says it is faster than logical migration, especially for large databases, but does not establish that it suits every engine configuration or source topology. Test the method you select on a representative dataset and verify that the objects your application needs are included.
For PostgreSQL sources
AWS describes three broad homogeneous migration patterns for PostgreSQL: a native or third-party full load, AWS DMS full load with ongoing replication, and a native full load followed by DMS replication. The hybrid route separates the initial data load from ongoing change capture. Select among them based on your permitted load window, replication needs, and operational capacity.
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For a move that needs ongoing replication
An initial load transfers the existing data; ongoing replication captures subsequent source changes so the target can catch up before traffic moves. This can support a near-zero-downtime goal, but it does not prove that an individual cutover will have zero downtime or be risk-free. You still need to measure lag, verify consistency, test the application, and define a fallback.
Rehearse the full sequence with your actual source and target configuration. Measure initial-load time, replication lag, validation work, and cutover tasks rather than relying on a generic duration. AWS’s migration FAQ says most customers complete an Aurora migration in under an hour, while noting that time depends on database format and dataset size. That broad statement is not an estimate or promise for a particular production database.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Prepare and execute the cutover
Use a controlled cutover plan appropriate to the application and the chosen migration method. For a replication-based move, define in advance what lag is acceptable and how writes will be paused or redirected. Assign an owner to make the go/no-go decision and an owner for each operational task.
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- Confirm readiness: Verify target consistency, required schema and code objects, permissions, credentials, application connection settings, and monitoring.
- Check replication: Review lag and confirm the target has received the changes required for cutover. Do not switch traffic against an untested assumption about catch-up.
- Control writes and switch traffic: Follow the rehearsed write-freeze or application-specific cutover procedure, then direct the application to the target.
- Validate behavior: Test representative reads and writes and confirm application behavior and monitoring after the switch.
- Apply the rollback decision: Use the pre-agreed criteria and procedure if validation fails. Do not assume that changing the connection back is safe after writes have begun on the target; account for how any target-side changes will be handled.
How to use AWS migration time claims
Migration duration varies with data size and format, method, network conditions, and operational work. AWS’s FAQ statement that most customers finish in under an hour is qualified by those factors and should not replace a rehearsal on your workload. The three-hour estimate in AWS’s SQL Server-to-Aurora PostgreSQL walkthrough refers to the introductory exercise, not to moving a production database.
AWS published the Aurora MySQL Migration Handbook on April 29, 2022. It can provide background, but use current AWS documentation to verify compatibility, service behavior, and Region availability before a production migration.
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