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Reworked the guide around current AWS migration choices, data validation, and cutover planning.
Migrating content to AWS means moving data and changing the applications or people that depend on it. The right method depends on what you have: shared files, Amazon S3 objects, a database, or a content application that combines all three. A successful copy alone does not prove that permissions, metadata, application behavior, or recent writes made it across.
This guide focuses on planning and transferring content, verifying the destination, and handing traffic over safely. Moving application code or redesigning the application may need a separate migration plan.
Set the migration boundary before choosing a tool. Decide which content is in scope, who owns it, which system is authoritative during the move, and what evidence will count as a successful migration. Agree on an acceptable cutover window and rollback point with the people responsible for the application and data.
Build an inventory from the source rather than relying on estimates. Record the data size and item count, growth rate, file or object formats, database engines, and the source and destination locations. Include the details that can affect whether the content still works after a copy:
Define the source consistency point. If the source supports snapshots, start the initial copy from a point-in-time snapshot and record how writes after that point will be captured. Make database snapshots application-consistent using the database’s own procedures. A storage snapshot alone might not align a content management system’s database with its media files.
For a content management system, include the database, uploaded media, user records, extensions, URLs, and application runtime. Moving only the media directory can leave pages with broken references or stale permissions.
Do not treat file copying, object replication, and database migration as the same job. Choose a method for each source and verify that it supports your exact source, destination, account, Region, metadata, and consistency needs.
Use AWS CloudFormation to describe and provision AWS resources such as buckets, roles, and networks. It does not copy the content stored in those resources; choose a data-transfer method separately. See how CloudFormation provisions resources.
Design access for the transfer and for the destination application separately. Give the migration role only the source read and destination write permissions it needs, limit access to the required buckets, prefixes, tables, or shares, and remove temporary access when the work is complete. For S3, check both identity and bucket policies, encryption-key permissions, and public-access settings.
Use the data volume divided by measured sustained throughput as a starting estimate, then add time for reconciliation and retries. A calculation based on link speed alone is only a theoretical minimum. Elapsed time also depends on item count, small-file overhead, source and destination limits, available network capacity, parallelism, metadata operations, and verification.
Test data that reflects the real mix of large and small files or objects, permissions, metadata, and write activity. Record the actual rate and failures, then use that result to schedule the initial copy and the final reconciliation. Include service requests, retrievals, cross-Region transfer, and network charges in the migration estimate; a transfer tool does not make those costs disappear.
Run a small pilot first and inspect both its data and its reports. Once the mapping and permissions are correct, copy the initial dataset while the source remains authoritative. Repeat incremental transfers or use a supported change-capture method to reduce the amount of data left for cutover.
Make source-change behavior explicit. Decide whether a source deletion should delete the destination copy, whether overwrites are allowed, and how to handle objects or files changed during a transfer. Configure and test these choices on sample data before using them at scale. AWS DataSync offers configurable integrity checks and task reports that show what a task attempted to transfer, skip, verify, or delete.
For a supported database migration, AWS DMS can load existing data and optionally capture changes from the source. Review the engine-specific requirements, schema conversion, indexes, procedures, sequences, and application connection settings separately. AWS DMS change data capture is not real-time and has no latency SLA; lag varies with workload and capacity. Wait for the agreed consistency point before redirecting writes, and rehearse that handoff with production-like data. See the AWS DMS migration phases and its CDC latency guidance.
Use evidence suited to the data. Compare manifests, expected counts, byte totals, and versions where those fields apply. For file systems, check paths, permissions, ownership, and metadata the application relies on. For databases, reconcile key tables and records, validate application-level queries, and check any supported migration validation results. Open representative files and objects using the destination application’s real permissions.
DataSync verifies data during transfer and lets you choose additional end-of-transfer checks. Its task reports provide a useful record of transferred, skipped, verified, and deleted items. Review the report and resolve failures or intentional exclusions before cutover; a successful task status alone does not validate application behavior or satisfy every data requirement. See AWS documentation for DataSync verification options and task reports.
Online copying and change capture can reduce the amount of interruption, but they do not promise zero downtime. The result depends on the application, supported change-capture behavior, final write freeze, and tested cutover process.
Write down how rollback works before the migration. Redirecting traffic back is safe only if the old source still has the required data. If users have written new data to the destination, define how to reverse or reconcile those writes before returning to the source; otherwise the two systems can diverge.
After cutover, continue checking application behavior, data access, transfer or replication lag, and cost. Set lifecycle policies from measured access patterns and actual retention requirements rather than arbitrary age thresholds. Keep the old source and migration evidence until the rollback and retention periods end. Then remove temporary credentials, agents, schedules, and duplicate copies that are no longer needed.
For workload-level checks after the move, see our guide to benchmarking AWS performance after migration.
DataSync transfers supported file and object data. Use a database migration tool such as AWS DMS or a database-native method for tables, schema, and database changes.
Live replication handles new and updated objects after the rule is configured. Use S3 Batch Replication to copy eligible existing objects.
No single transfer service guarantees that. A tested incremental copy or change-capture process may shorten the interruption, but the application’s write behavior and final consistency check determine the cutover window.