Large-scale application migration is a portfolio decision, not a single move that every system must make. Assess each workload, choose whether to retire, retain, move, replace, optimize, or redesign it, then organize the work into migration waves. For each wave, define ownership, data-consistency checks, cutover conditions, and a recovery plan that accounts for changes made after the target goes live.
What does changing the application engine mean?
It means moving a workload to a different execution environment or changing how it is built and operated. Those are related but distinct decisions: an application can move with little change, or its platform and internal design can change as part of the move. The phrase does not name one standard migration method, and a large portfolio usually contains several kinds of work.
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Do not assume that migration means converting every system to microservices. Some applications may be better retired, retained where they are, or replaced with a product. Others may need only a platform move or selected optimizations. A smaller, justified subset may warrant substantial architectural change.
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Which migration strategy fits each workload?
AWS groups portfolio choices into seven strategies, often called the seven Rs. They are options, not mandatory stages or a universal sequence. Use the distinctions to describe the intended outcome for each workload:
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| Strategy | What changes | When it may fit |
|---|---|---|
| Retire | The workload is shut down. | It no longer needs to run or its function is no longer required. |
| Retain | It remains in its current environment. | There is a reason not to move it now, such as a dependency or constraint that makes a move unsuitable. |
| Rehost | The application moves with minimal application change. | The priority is moving the workload while keeping its design largely intact. |
| Relocate | Servers or other platform resources move to a different platform boundary. | The required move concerns the platform resources rather than a deliberate redesign of the application. |
| Repurchase | The existing application is replaced with another product. | A replacement product can meet the business need better than moving the current application. |
| Replatform | The application moves with selected changes or optimizations. | A limited set of changes can improve the target fit without a broad redesign. |
| Refactor or re-architect | The application’s architecture changes more substantially. | The desired business or technical outcome warrants the broader change and its additional complexity. |
AWS describes refactoring as the most complex and costly of these strategies and advises against treating it as the default for a large migration. That is AWS guidance, not a rule that applies identically to every organization. Make the choice workload by workload, based on the outcome sought and the cost and risk the organization is prepared to accept.
What should be assessed before workloads are grouped into waves?
Discovery should inform both the strategy and the batch plan. A workload that appears simple to move can become a poor candidate for a particular wave if its dependencies, data, or operational requirements are not understood. Google Cloud’s workload-assessment guidance recommends grouping workloads and data into batches, choosing batch order, and sequencing work within each batch; it also calls for refining the plan as understanding improves.
- Business fit and expected value: What outcome should the move deliver, and does it justify the effort?
- Dependencies: Which applications, shared services, databases, and teams must be ready for the workload to move?
- Data and workload size: How much must be transferred, and what ongoing changes must be captured during the move?
- Downtime tolerance: What interruption can users and business processes accept?
- Change frequency: How often does the application or its data change, and how does that affect synchronization and release planning?
- Source and target environments: What differences in platform, configuration, or operating model must be addressed?
- Security and compliance: Do the target environment and migration process meet the workload’s requirements?
- Operational readiness: Can the receiving team monitor, support, secure, and recover the workload?
- Cost, schedule, and recovery: What is the expected effort, and how difficult would rollback or fail-forward recovery be?
Google Cloud notes that zero- or near-zero-downtime approaches are usually more costly and complex than approaches that permit downtime. Treat that trade-off as a business decision: add complexity only when the interruption requirement justifies it.
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How should migration waves and a migration factory work?
A migration factory is an operating model for making repeated migration work more consistent through automation, release planning, and concurrent teams. It is useful when discovery reveals recurring patterns that can be standardized. It is not a claim that every workload should pass through the same process or use the same architecture.
- Build a current workload inventory. Record the dependencies, data, requirements, owners, and intended strategy needed to make a credible plan.
- Identify repeatable patterns. Standardize tasks that recur, while keeping exceptions and complex application decisions visible rather than forcing them into a generic runbook.
- Form batches and sequence work. Choose batch order and the order of workloads within each batch. Revisit both as discovery changes the risk or readiness assessment.
- Set release plans and ownership. Give teams a workable schedule, clear responsibilities, and a backlog that can be reprioritized when dependencies or timing change.
- Match team structure to complexity. AWS guidance distinguishes high-volume, low-complexity rehost work from repeatable replatforming and complex or core refactoring that needs planned release cycles and application-owner participation.
- Review outcomes before scaling the next wave. Use what teams learn about dependencies, validation, and cutover to adjust the sequence and the level of standardization.
Google Cloud recommends technical designs, responsibility matrices, and timelines as useful planning artifacts. Batch sizes need not stay fixed: increase, reduce, or regroup them as the picture of workload readiness becomes clearer.
How can an application change incrementally?
When a workload needs architectural change, a gradual approach can let the existing application continue serving users while replacement capabilities are introduced. The Strangler Fig pattern does this with a façade or proxy between callers and the old and new implementations. Initially, most requests can still go to the old application; routing shifts as replacement capabilities become ready.
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That coexistence creates work of its own: teams need clear routing rules, observability across both systems, and explicit ownership of each capability. Incremental change is not automatically simpler or safer; its value depends on whether the steps reduce risk and produce useful improvements without leaving an indefinite tangle of old and new paths.
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In her 24 April 2018 article, “How to break a Monolith into Microservices,” Zhamak Dehghani wrote: “Each step of migration represents an atomic improvement to the overall architecture.” Dehghani’s perspective favors small, deliberate improvements, while also urging teams to consider whether decomposition is appropriate and whether microservices are the right destination. A common failure is to build a new service but never retire the old code path.
How should a shared database be migrated?
Moving application capabilities without addressing a shared monolithic database can leave new services coupled to old structures. Microsoft’s Strangler Fig example describes a staged path for one domain: first introduce a service that still uses the old database, then create a database isolated for that domain, load historical data with ETL, and synchronize ongoing changes with change data capture (CDC).
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- Establish the new service path. Keep the old database in use initially while the domain service is introduced.
- Create the domain database. Isolate the data needed by the new domain rather than assuming that the service boundary alone removes database coupling.
- Load historical data. Use an ETL process to populate the new database with the required existing records.
- Synchronize changes. Capture ongoing source changes with CDC so the target can catch up while the old path remains active.
- Validate before transferring responsibility. Check completeness and consistency before switching reads and writes to the new database.
- Retire old structures only when ready. Remove old tables, procedures, and dependencies after validation and after deciding that rollback no longer depends on them.
Microsoft warns that rollback becomes substantially harder after old objects and synchronization paths are removed: they may need to be restored and changes replayed. Treat cleanup as a deliberate decision point, not as a harmless final tidying step.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What makes a data migration consistent?
Before cutover, define what “complete” means for the dataset being moved. Google Cloud’s database migration guidance describes a consistent migration as one in which the selected data is complete, duplicates are absent, and source changes are applied in their original order. These properties need to be checked, not inferred merely because a transfer job reports success.
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- Duplicate handling: Decide how repeated delivery or replay is detected and handled.
- Change ordering: Preserve the order in which source changes occurred where order affects the result.
- Write ownership: Identify which system is authoritative for writes at each stage.
- Recovery point: Know how migration resumes after a failure from a state whose consistency can be established.
In an active-active arrangement, both systems accept writes. That can introduce conflicts, so define conflict-resolution rules rather than assuming that both copies will converge automatically. Google Cloud also distinguishes migration, which has an intended end state where clients use the target and the source may be turned down, from replication, which can continue without a planned completion point. For a near-zero-downtime transfer, source updates must be processed quickly enough to keep up; the migration design also needs a way to resume after failure of the migration system or a database.
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How should cutover and rollback be planned?
A cutover changes where clients send traffic and, often, which system owns writes. AWS cutover guidance lists an ingestion freeze, final backup, final data synchronization, and routing change among the activities to plan. The exact sequence depends on the workload, but it should be explicit enough that the team knows when the target is ready and who can authorize the switch.
- Freeze or control new ingestion when required by the chosen migration method, so the final synchronization can reach a known point.
- Take the planned final backup and confirm that it is usable for the recovery approach.
- Run final synchronization and verify the required completeness, ordering, and consistency checks.
- Switch routing according to the cutover plan, then monitor the target and confirm that the expected traffic and writes are reaching it.
- Use the agreed recovery path if the target does not meet acceptance conditions; do not assume that reversing the route reverses data changes.
A clean traffic reversal may be possible when no data has changed after cutover. If the target has accepted new transactions, the old source may be stale. Recovery may then require fail-forward replication, dual writes, or native backup and restore rather than simply pointing traffic back. AWS recommends testing backup and restore in lower environments before cutover.
For that reason, “zero downtime” and “instant rollback” are not meaningful guarantees without the actual write path, synchronization lag, consistency checks, and recovery procedure. Design and rehearse the recovery path before the cutover window, including how the team will identify the last known consistent point.
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