AWS database migration is the process of moving a database to AWS, usually with AWS Database Migration Service (DMS) for the data, native engine tools for same-engine moves, and AWS schema conversion tooling when the enginechanges. For production data, the outcome depends less on the tool than on the checks done before cutover: audit, validation and a rehearsed rollback.
Most database migrations go wrong before the data moves. A missing index. A permission nobody documented. A cutover plan nobody rehearsed. This guide walks through the pre-flight checks we’d want in place before moving production data, from choosing a migration path to proving the target matches the source and keeping a way back.
| What You’ll Learn | Why It Matters | What It Helps You Avoid |
| Migration paths and tools | Which approach fits your engine, data size and downtime tolerance | Choosing a tool out of habit |
| The source database audit | What to inventory before anything moves | Surprises from undocumented dependencies |
| What DMS leaves behind | The objects and settings that need separate handling | Missing indexes, keys and users at go-live |
| Testing and validation | How to prove the target matches the source | Finding bad data after cutover |
| Cutover, rollback and aftercare | How to keep downtime short and recovery possible | A one-way door with no plan B |
Which AWS database migration path fits your workload?
The right path depends on three things: whether the database engine stays the same, how much data is moving, and how much downtime the business can tolerate. Same-engine moves can use native database tools or AWS Database Migration Service, while a change of engine adds a schema conversion step.
Start with the engine question. A homogeneous migration keeps the engine (MySQL to MySQL, PostgreSQL to PostgreSQL), so you can export the schema with the engine’s own tools. A heterogeneous migration changes engines, SQL Server to PostgreSQL for example, and needs the AWS Schema Conversion Tool or DMS Schema Conversion to translate tables, code and data types.
Then the downtime question. DMS runs three kinds of tasks: full load, which copies existing data once; full load plus change data capture (CDC), which copies existing data and keeps replicating changes while the source stays live; and CDC only, for when the initial copy has already happened. That middle option is what makes short cutover windows possible. Native dump and restore is simpler, but downtime grows with the size of the data.
| Approach | Best for | Downtime profile | Watch out for |
| Native engine tools (dump and restore) | Same-engine moves with smaller datasets or a generous maintenance window | Grows with data size | You own all the validation |
| AWS DMS, full load plus CDC | Production systems that can’t go dark for long | A short window around cutover | Secondary objects such as indexes and foreign keys need separate handling |
| Schema conversion plus DMS | Engine changes, such as commercial to open-source databases | Depends on conversion and testing effort | Converted code needs manual review |
Destination matters as much as route. Running the database on Amazon EC2 keeps full control and full responsibility, while Amazon RDS and Aurora hand patching and backups to AWS. The right set of AWS cloud solutions usually follows from how much database administration your team wants to keep doing.
What should you audit in the source database before moving anything?
Inventory what is in the database and, more importantly, what depends on it: engine and version, size and growth rate, schema objects, user accounts and permissions, scheduled jobs, connection patterns and every downstream consumer. Then confirm the source is configured for replication.
The dependency list is where migrations get humbled. Everyone remembers the main application. Fewer remember the reporting tool with its own read-only login, the nightly export that feeds the finance spreadsheet, or the old script on a forgotten server that updates one table at 2 a.m. Find them now. Finding them after a cutover means finding them through a complaint.
Replication prerequisites are the second checklist, and DMS’s premigration assessments spell them out per engine. For a MySQL source, change data capture needs binary logging in ROW format with full row images, and the DMS user needs replication privileges. For SQL Server, the assessments flag features such as computed columns and temporal tables and check that at least one full backup exists. These are exactly the settings that look fine until the CDC task refuses to start at midnight.
Size the job too. Note how long a full load of your largest tables takes in rehearsal, how fast the source grows each week and how much sustained bandwidth sits between the two environments. A migration plan with no transfer-time estimate is a wish.
What does AWS DMS actually migrate, and what does it leave behind?
AWS DMS migrates table data and creates the target tables with their primary keys, and in some cases unique indexes. It doesn’t create secondary indexes, non-primary-key constraints or column defaults, and AWS’s best-practices guidance adds that it doesn’t automatically create foreign keys or user accounts.
Myth vs. Fact
Myth: When the DMS task shows 100%, the database has been migrated.
Fact: The task has copied table data and primary keys. Secondary indexes, foreign keys, user accounts, and, depending on the route, triggers, views and stored procedures need separate handling, and accuracy needs separate proof.
The consequences are quiet ones. Without foreign keys, referential integrity disappears and nothing complains. Without secondary indexes, queries that took milliseconds start to crawl. Without user accounts, the application can’t log in on go-live morning. Meanwhile the dashboard reads 100% the whole time.
How you fix it depends on the migration type. For same-engine moves, export the schema alone with the engine’s native tools (a schema-only pg_dump for PostgreSQL) and apply it to the target first. For engine changes, the Schema Conversion Tool or DMS Schema Conversion generates the target schema and converts code, as AWS’s knowledge center explains. Treat the conversion report with some suspicion. A headline conversion percentage can flatter the result, so have someone who knows the application read the converted procedures.
Sequence matters as well. Secondary indexes can slow the bulk load, which is why DMS’s premigration assessments flag them. Yet the change-capture phase can crawl without indexes that support updates and deletes. The usual compromise is to build them after the full load and before CDC begins, with foreign keys and any stragglers on the cutover checklist.
How do you test the migration with production-scale data?
Run a premigration assessment, rehearse the full migration at least once on a production-sized copy, switch on data validation, and test the application against the target under realistic load. A migration tested only on a sample is a guess about production.
DMS offers two cheap safeguards here. The premigration assessment warns about likely problems before the task starts, and the validation option checks that data arrived accurately. Both take little effort, and both get skipped more often than they should.The rehearsal answers questions nothing else can. How long does the full load really take? How far does replication lag drift at peak hours? Which queries behave differently on the new instance or engine? Write the numbers down, because they become your cutover schedule.
Row counts and checksums on critical tables are a start. Business reconciliation is better. Ask finance to compare three familiar reports on both systems, and ask operations to run a typical day’s transactions. People who know what the numbers should look like catch what scripts won’t.
Scale changes what checking means. On one project we had to move 50,000+ employee records into SAP Fieldglass under a compliance deadline, with data spread across legacy systems and unstructured formats. Manual checking was impossible, so extraction, validation and upload were automated together. That wasn’t a database migration, but the lesson carries: if you can’t verify a data set by eye, build the verification before you build the move.
How do you handle security, network and compliance before the data moves?
Treat the migration path as production: private connectivity where possible, encryption in transit and at rest, least-privilege access for the migration user, and a deliberate choice of AWS region for any regulated data.
Migration accounts tend to be overprivileged because it’s faster. Give the DMS user the rights its tasks need (the premigration assessments check precisely this), then revoke anything elevated afterwards. Turn on encryption for the endpoints and the target storage, and keep a log of who touched what during the window.
Rehearsal copies deserve the same care. Production data in a test environment is still production data, so restrict access or mask sensitive fields. Region choice also reaches beyond latency. Data residency rules in your sector or country may dictate where the data can live, and moving a finished database to another region is its own migration, so settle it before you build the target.
Then the pipe itself. Divide data size by sustained throughput and see whether the answer fits your window. If it doesn’t, plan more bandwidth, a longer replication runway or an offline transfer option, rather than hoping.
How do you plan cutover and rollback so downtime stays short?
Use full load plus change data capture so the source stays live while the target catches up, then schedule a brief write freeze, confirm replication lag has reached zero, validate, switch connections and keep a tested way back.
The Cutover Path at a Glance
Audit source → Pre-create schema → Full load → CDC catch-up → Validate → Freeze and cut over → Monitor → Retire source
In its step-by-step migration guides, AWS keeps the cutover list short: stop the DMS task, create any foreign keys and secondary indexes you hadn’t already, then validate tables, views, procedures, functions and triggers in the target schema. Short list, but every line on it assumes you rehearsed it.
Rollback usually gets less rehearsal time than it deserves. Keep the source untouched, ideally read-only, until the new environment has passed a stability window agreed in advance. Where the engine allows it, replicating changes back to the source keeps a rollback path open after users start writing to the target. Settle the abort criteria before the weekend, not during it: what error rate, what lag, what failed check means you go back.
Short downtime is mostly preparation. After a detailed architecture and security evaluation, our cloud enablement work for a hospitality client kept migration downtime as low as 10 minutes. When a major UAE food and beverage company’s website went down in a sudden power failure, a pre-configured replica in Singapore restored it within minutes. The same thinking sits behind an airline disaster recovery setup we built on AWS across multiple availability zones, with backups spanning regions: the standby is ready before anyone needs it. A migration plan with no recovery plan is half a plan.
What should you monitor and manage after the database moves?
After go-live, watch performance against your pre-migration baseline, confirm backups and failover work, track storage growth and spend, and finish anything you deferred, such as indexes and constraints. The first few weeks are when a new environment shows its real behavior.
Capture query latency, CPU, connections and I/O before the move, so afterwards you have something to compare against. Amazon CloudWatch supplies the metrics on the AWS side. Without a baseline, “it feels slower” is an argument nobody can win.
Check the unglamorous safety nets too: automated backups, snapshot retention and, for production on Amazon RDS, a Multi-AZ configuration. Test a restore once. A backup nobody has restored is an assumption. Keep an eye on the bill as well, since instance sizes chosen from guesses tend to look different once real traffic arrives.
This is where AWS managed services earn their place. Right-sizing, patching, alerting and tuning are continuous jobs, and they compete with everything else your team is doing. Some businesses keep that in-house, and it works when someone clearly owns it. Others hand it to a partner so someone is watching around the clock. Codelattice’s AWS cloud services and DRaaS practices are built around that ongoing work.
How do you choose an AWS partner for a production database migration?
Look for demonstrated experience with your database engine, certified engineers who will do the work, a written runbook that includes rollback, and clear post-migration support terms. A logo on a website covers none of those. Ask for specifics. Which engines have they migrated, at what size, and what downtime did they actually achieve? Who will be on your project, and are they AWS-certified? Can they show a rehearsal plan and a rollback plan on paper? What happens when something breaks at 2 a.m. after go-live: response times, support hours, languages?
A good AWS partner will tell you what they’d check first, and will say so if your timeline is too tight. Be wary of anyone who skips straight to the tool.
30-Second Readiness Check
- Have you inventoried every application, job and report that reads from or writes to the database?
- Have you run a full rehearsal on production-sized data and recorded how long it took?
- Does a written rollback plan exist, and has anyone practiced it?
- Can you prove the target matches the source with automated checks rather than eyeballing?
- Is a named person responsible for the first 30 days after go-live?
Four or more yeses and you’re close to scheduling the cutover. Fewer than four, and it’s cheaper to fix the gaps now than on the night.
Moving production data deserves a second pair of eyes. As an AWS cloud partner, Codelattice offers a free consultation to review your database migration plan. We handle migration, onboarding and ongoing support, backed by a lightning-fast SLA and multi-language support. Write to askus@codelattice.com and tell us what you’re moving.
