Case study: migration to a data center

An anonymous data-center migration scenario: dependency mapping, replication, cutover, rollback and acceptance.

Anonymous implementation scenario

Case study: migration to a data center

A business moves applications, databases and supporting services from an ageing server room or another provider. The migration is split into discovery, replication, rehearsal, cutover and stabilization.

This is an anonymous implementation scenario based on the standard DataHouse delivery process. It does not identify a customer or claim an unverified production result.

Starting context

The source environment contains undocumented dependencies, mixed operating systems and external DNS, mail and integration endpoints.

Implementation challenge

The main risk is not copying virtual machines but preserving data consistency, identities, certificates, DNS, firewall rules and the ability to return to the source environment.

Discovery before design

Inventory workloads, owners, data volumes, versions and support constraints.

Map application, database, identity, DNS, mail and third-party dependencies.

Define target RPO/RTO, maintenance window, acceptance owners and rollback point.

Measure bandwidth and replication duration using representative data.

Implementation path

01

Build the target network, systems, access roles, monitoring and backup path.

02

Seed data and repeat incremental replication while the source remains active.

03

Run a rehearsal with technical checks and business owners before the final window.

04

Freeze changes, perform final synchronization, switch traffic and stabilize with rollback readiness.

Acceptance criteria

  • Data consistency and application transactions are confirmed by owners.
  • DNS, certificates, integrations, mail and remote access work on target paths.
  • Monitoring, logs, backup jobs and alert escalation are operational.
  • The rollback decision point and source-retirement evidence are documented.

Operating model after handover

  • Use a stabilization period with enhanced monitoring and controlled change intake.
  • Close documentation gaps discovered during migration.
  • Run backup restore and incident-contact tests after handover.
  • Review capacity and service-specific SLA after real production measurements.

Verified DataHouse facts

The links below separate public operator and infrastructure facts from customer-specific scope, pricing, SLA and outcomes.

Frequently asked questions

Does this page describe a named customer?

No. It documents an anonymous implementation pattern and does not publish customer identity or unverified results.

Can every migration be performed without downtime?

No. Downtime depends on application design, data consistency, dependencies and the available replication method.

What determines the final migration plan?

The workload inventory, dependency map, data volume, bandwidth, acceptance criteria, RPO/RTO and rollback requirements.