GCP migration services in India move your applications, databases and files onto Google Cloud in planned waves, with each cutover rehearsed and reversible. A sound migration starts with an honest inventory, groups workloads by dependency and risk, and treats rollback as a design requirement rather than an emergency plan. Here is how each stage should run.
Stage one: assessment before any data moves
Most migration overruns trace back to something nobody knew existed: a nightly batch job on a forgotten server, a hard-coded IP address in a partner integration, a licence tied to physical hardware. Assessment exists to find these early.
Inventory and dependency mapping
List every server, database, storage share, scheduled job and external integration. Discovery tools and network flow logs show which systems actually talk to each other, which is often different from the architecture diagram. Interview application owners as well; tools miss business context, such as a quarter-end report that only runs four times a year.
Choosing a path for each workload
Each workload gets a disposition. The common options are:
- Rehost: lift the virtual machine as-is onto Compute Engine. Fastest, least change.
- Replatform: small changes for a managed service, such as moving a self-managed database to Cloud SQL or AlloyDB, or a container to GKE or Cloud Run.
- Refactor: redesign for cloud-native services. Highest effort, reserved for systems that justify it.
- Retire or retain: switch off what nobody uses, and leave on-premises what has a reason to stay.
Once you know your estate, talk to us about a migration assessment and we will help you set the disposition for each system.
Stage two: build the target and plan the waves
Landing zone first
Before the first workload lands, the Google Cloud organisation needs its folder structure, identity integration, networking, hybrid connectivity through Cloud VPN or Interconnect, logging and security guardrails, all defined in Terraform. Restricting resource locations to the Mumbai and Delhi regions helps with data residency expectations.
Wave planning
Group workloads into waves that move together because they depend on each other. A good first wave is low-risk and representative: an internal tool or a non-critical service that exercises the network, identity and monitoring set-up. Later waves carry the heavier systems once the team and tooling have been proven.
What each wave plan should record
- Applications and databases in scope, with owners named.
- Migration method and tooling for each item.
- Test cases the business will run to accept the move.
- The cutover window, communication plan and freeze period.
- Rollback triggers and the exact rollback steps.
The tooling Google Cloud provides
Google offers purpose-built services that reduce manual effort. Migrate to Virtual Machines replicates VMs from VMware, AWS or Azure into Compute Engine, allowing test clones before the final switch. Database Migration Service handles continuous replication for MySQL, PostgreSQL and SQL Server into Cloud SQL, and supports Oracle to PostgreSQL conversion paths. Storage Transfer Service moves file and object data, and the BigQuery Data Transfer Service helps bring warehouse data across. For very large datasets with limited bandwidth, Transfer Appliance ships data physically. The right mix depends on data size, acceptable downtime and the source platform.
Where GCP migration cutovers go wrong
Cutover is the risky hour. The usual failure points are predictable, which means they can be planned for:
Data drift and replication lag
If the source keeps accepting writes while replication catches up, the final sync takes longer than planned. Measure lag during rehearsals and schedule a short write-freeze.
DNS and hard-coded endpoints
Lower DNS time-to-live values days in advance. Search configuration files and partner integrations for IP addresses and hostnames that will not follow the DNS change.
Performance surprises
Latency between an application now in the cloud and a database still on-premises can cripple response times. Keep chatty components in the same wave.
Licensing and support
Some commercial software licences are tied to hardware or specific clouds. Check before the wave, not during it.
Rehearse every cutover at least once in a copy environment, time each step, and hold a go or no-go meeting with clear criteria. If rollback is needed, the source systems should still be intact and reachable. Our article on disaster recovery as code explains how to design recovery objectives into the new environment so the move improves resilience instead of only relocating it.
After the cutover: stabilise, then optimise
A migration is not finished when traffic switches. Plan a hypercare period for each wave in which the migration team watches error rates, latency and job completion closely and fixes issues quickly. Only after the workload is stable should you decommission the source systems, and only after backups in Google Cloud have been restored successfully in a test. Then turn to optimisation: rightsize machines using real utilisation data, move suitable workloads to managed services, apply labels and budgets, and review whether steady workloads justify commitments.
Choosing GCP migration services in India: how iJurug Soft works
iJurug Soft offers cloud management services across GCP, AWS and Azure, delivered by senior engineers from Bangalore. We run migrations through our Discover, Design, Build, Launch and grow process with fixed milestones: assessment and dispositions, landing zone and wave plan, rehearsed cutovers, then optimisation and long-term support. Security is designed in from the landing zone onward. If part of your estate is a monolith you want to modernise during the move, see our guide to migrating a legacy monolith to microservices before deciding what to refactor.
Frequently asked questions
How long does a migration to Google Cloud take?
It depends on the number of workloads, their dependencies, data volumes and how much refactoring you choose. Assessment gives a realistic timeline; a small, well-understood estate moves far faster than a large one with many integrations.
Can we migrate with no downtime at all?
Some workloads can move with only a very brief interruption using continuous replication. Others need a short planned window. The plan should state the expected interruption for each system.
Should we refactor everything while we migrate?
Rarely. Changing platform and architecture at once multiplies risk. Rehost or replatform first, stabilise, then refactor where the business case is clear.
Planning a move to Google Cloud? Share your current environment, key applications and timing constraints through our contact form or write to info@ijurugsoft.com. We will come back with an assessment approach, a draft wave outline and a clear quote.