A platform migration, a version upgrade, and a move to cloud are one engagement wearing three labels. Each is a change made once, under a deadline, to the system everything else depends on. Fortified Data plans each against a measured baseline, proves it in a staging environment first, and establishes the rollback path before the change rather than during it.
A tested rollback path at every phase. Senior US-based consultants.
Why it matters
Migrations rarely fail on the data movement. They fail on what nobody checked first.
Jobs, linked servers, permissions, and dependencies that were never written down are found at cutover, when the only options left are to improvise or to stop. Compatibility analysis is the cheapest part of the project and the part most often skipped.
Without a measured baseline from before the move, nobody can establish whether the target is slower, so the argument about performance afterwards is conducted on impressions and never actually settles.
A cutover with no rehearsed way back is a decision taken once, under time pressure, by whoever is awake. Rollback designed in advance is what keeps a bad night from becoming a bad quarter.
Database migration and upgrade services are fixed-scope engagements that move a database to a different platform, a different version, or a different place to run, with the data intact and the interruption planned. Fortified Data delivers SQL Server version upgrades, SQL Server to Azure SQL and Azure SQL Managed Instance, analytics migrations to Azure Synapse, and MySQL major-version upgrades, alongside cloud migrations to AWS, Microsoft Azure, and Google Cloud, across Microsoft SQL Server, Azure SQL, Oracle, PostgreSQL, and MySQL.
Every engagement carries an inventory and dependency review, rehearsed dry runs, data validation appropriate to the platform and migration method, and a documented rollback path.
These three arrive as separate requests and run as the same work. The object inventory, the compatibility analysis, the transformation rules, the staging validation, and the cutover sequence are the same whether the target is a newer version, a different engine, or a managed service in another provider's data center.
Migrations rarely fail on the data movement. They fail on the object nobody inventoried, the application that depended on behavior the new platform changed, the performance profile that was never baselined so nobody could tell whether the target was slower, and the absence of a way back once the cutover window closed. Those four are what the phases below exist to remove.
Six phases. Each produces evidence before the next begins, which is why the rollback path is designed before the first change rather than improvised after the first problem.
Inventory of database objects, applications, and dependencies, and analysis of where the target behaves differently from the source. The purpose is to find what breaks while nothing has moved yet.
Scope, timeline, resources required, and risks, written into a migration plan rather than agreed in a meeting. The plan names the success criteria that the final phase tests against.
Object and data type mapping, code conversion, and transformation rules documented and repeatable, so the migration can be run more than once and produce the same result each time.
A reversal procedure for each phase, established and tested before that phase runs. A decision to reverse then follows a documented procedure rather than improvisation at two in the morning.
Unit, integration, and performance testing in a staging environment, with row-level validation and checksum verification against the source, before anything is approved for cutover.
A sequenced cutover inside your maintenance window, then post-migration verification against the criteria agreed at the start and a performance comparison against the original baseline.
Your application team updates connection strings and tests the applications; we coordinate the cutover with them.
Documents, not a verbal handover. Each one is written so the team that owns the environment afterwards can use it without the consultant in the room.
Scope, sequence, timeline, risks, acceptance criteria, and the rollback steps for cutover.
The scripts used for the move, kept as repeatable assets.
The new environment operating and verified before it is accepted.
Host names, IP addresses, listeners, and critical services for the new environment.
What your team needs to operate the new environment.
Monitoring through the stabilization period and project close-out.
Row-level validation and checksum verification against the source, in staging and again at cutover. A successful job status is not treated as evidence that the data arrived.
Every phase has a tested reversal procedure written before the phase runs, so reversing is a decision the team makes calmly rather than a problem it solves under pressure.
SQL Server version upgrades, SQL Server to Azure SQL and Azure SQL Managed Instance, analytics migrations to Azure Synapse, and MySQL major-version upgrades, run by consultants who have worked both sides of the move.
Workload analysis sets the target specification from measured load rather than from the current server's spec sheet, which is where most of the cost of a cloud migration is decided.
The same firm runs environments like yours every day under managed services, so the target architecture is judged on whether it can be maintained and not only on whether it can be built.
The consultant who designs the migration is still on it at cutover. The architecture does not change because a resource did.
AWS, Azure, and Google Cloud
A move to AWS, Microsoft Azure, or Google Cloud is a migration with a monthly bill attached. The data movement is the well understood part. What decides whether the move was worth making is the size of the target, and that is settled before the commitment rather than in the first invoice review.
Existing workloads, dependencies, and risks evaluated into a roadmap and a business case, including which systems should be redesigned rather than lifted across as they are.
Target instance classes, storage tiers, and capacity set from measured utilization rather than from the specification of the server being replaced. Overprovisioning carried into cloud is billed every month it survives.
Secure, scalable cloud-native design on Azure, AWS, or Google Cloud, with high availability, disaster recovery, and the compliance controls in scope for your data built into the design rather than added afterwards.
Structured and unstructured data moved with the same row-level validation, checksum verification, and phase-by-phase rollback coverage used on any other migration, with encryption in transit and at rest.
A post-migration review of spend and performance, with ongoing monitoring available through our managed service plans.
An upgrade moves a database to a newer version of the same platform. A migration moves it to a different platform, a different host, or a cloud provider. Fortified Data runs both through the same six phases, because the compatibility analysis, staging validation, rollback design, and cutover sequence are the same work in each case. An upgrade usually has a shorter compatibility list. It does not have a different method.
Microsoft SQL Server, Azure SQL, Oracle, PostgreSQL, and MySQL. The delivered work is SQL Server version upgrades, SQL Server to Azure SQL and Azure SQL Managed Instance, analytics migrations to Azure Synapse, and MySQL major-version upgrades. Fortified Data staffs mixed-platform projects with senior US-based consultants under one engagement and one point of accountability, adding US-based specialist partners only when a project calls for them.
It depends on the method the environment can support. A backup and restore takes as long as the data takes to move. Replication-based and log-shipping approaches keep the target synchronized while the source stays live and reduce the cutover to a switch, at the cost of a longer preparation phase. Fortified Data establishes which methods are available for your platform pair during the assessment, prices the difference, and plans the cutover around your maintenance window.
Every phase of a Fortified Data migration has a reversal procedure that is written and tested before that phase runs, including the cutover itself. The runbook names what triggers the decision to reverse, who makes it, and how long the reversal takes. Because the procedure exists in advance, reversing is an operational decision rather than an incident.
Fortified Data validates at row level and verifies with checksum comparison against the source, first in the staging environment during multi-phase testing and again at cutover. Unit, integration, and performance testing run alongside it, so the target is confirmed to hold the same data and to behave the same way under load before the environment is accepted.
Yes. Cloud migration is delivered inside this service line rather than as a separate practice, covering cloud readiness assessment, target architecture design, data migration, and a post-migration review of spend and performance on AWS, Microsoft Azure, and Google Cloud. What separates it from an on-premises migration is workload right-sizing and the cost model, which is why both are explicit phases of the cloud work.
Before. Sizing a cloud target from the current server's specification carries the existing overprovisioning onto a platform that bills for it every month, and correcting it afterwards becomes a second project with its own downtime. Fortified Data analyzes measured workload during the assessment and sets the target specification from what the system actually uses, then monitors spend after cutover so the sizing holds.
The engagement ends with verification, documentation, and knowledge transfer to whoever operates the environment next. That can be your own team, or Fortified Data's managed services if you would rather the result was held than handed back. The choice is made at handoff rather than assumed at the start, and the documentation is written to support either one.
The migration is a project with an end date. If you would rather the environment was held afterwards than handed back, these are the routes.
We run your environment, with 24/7 monitoring, maintenance, and proactive improvement.
A guaranteed 15-minute emergency response plus monthly on-demand hours that bank when unused.
Project-based engagements for migrations, performance tuning, data modernization, business intelligence, and high availability and disaster recovery.
The first conversation is about the source, the target, the window you have, and what the environment is actually using. The plan and the estimate follow the assessment, so the date is set against evidence.
Let us show you what's possible.