Seamlessly Migrating from Azure to GCP: A Step-by-Step Guide for Technical Teams

As enterprises increasingly diversify their cloud strategies, understanding how to efficiently migrate workloads from Azure to Google Cloud Platform (GCP) is essential for minimizing downtime, cost, and complexity. While many assume cloud migrations are inherently risky and complex, a well-planned Azure to GCP migration can not only streamline operations but also unlock unique GCP capabilities that accelerate innovation.

In this post, I’ll walk you through a practical, step-by-step approach to migrating your workloads from Azure to GCP — designed specifically for technical teams ready to take control of the process.

Why Migrate from Azure to GCP?

Before diving into the how-to, let's quickly cover why some organizations choose GCP over Azure:

• Superior AI/ML Integration: Google leads in AI and machine learning services like Vertex AI.
• Data Analytics Excellence: BigQuery offers powerful serverless analytics.
• Innovative Kubernetes Support: GKE (Google Kubernetes Engine) is considered one of the best managed Kubernetes services.
• Cost Efficiency: Sustained use discounts and custom machine types often reduce costs.
• Global Network Infrastructure: Google’s backbone network promises low latency and reliability.

If you’re aiming to leverage any of these advantages, migrating makes strategic sense.

Step 1: Prepare Your Migration Strategy

A successful migration begins with detailed planning.

Assess Existing Workloads

• Inventory all your running Azure resources (VMs, databases, storage accounts, app services).
• Categorize by dependency and criticality.
• Evaluate which resources can be moved "as-is" versus those needing re-platforming or refactoring.

Define Migration Goals

• Minimize downtime?
• Reduce costs?
• Improve performance?
• Implement better security or governance?

Documenting goals informs your choice of tools and approach.

Step 2: Map Azure Services to GCP Equivalents

Understanding service parity is key. Here's a quick mapping for common Azure services:

This will help you identify where you’ll need to make adjustments in architecture or code.

Step 3: Setup Your GCP Environment

Before migration, prepare your destination environment:

1. Create a new GCP project with appropriate billing.
2. Configure IAM permissions so your team can provision resources.
3. Enable APIs relevant to your workloads (Compute Engine API, Cloud Storage API, etc.).
4. Set up networks and firewall rules similar to your current setup.
5. For database migrations, configure the target database instances (e.g., provision Cloud SQL with the right specs).

Step 4: Migrate Compute Resources

Migrating VMs

For migrating virtual machines from Azure VM to Compute Engine:

• Export your VM disk as a VHD file using Azure’s export tool.
• Upload this VHD file to Google Cloud Storage via gsutil.
• Import the disk image into Compute Engine using:

gcloud compute images import [IMAGE_NAME] \
  --source-uri gs://[BUCKET_NAME]/disk.vhd \
  --os windows-server-2019

Once imported, create an instance using this image.

Example: Migrating a Windows Server VM hosting legacy applications while maintaining configurations.

Migrating Containers or AKS Clusters

If you’re using containers or AKS:

• Export container images to a registry accessible by GCP — consider pushing them to Google Container Registry or Artifact Registry.

docker tag azurecontainerregistry.azurecr.io/myapp gcr.io/my-gcp-project/myapp
docker push gcr.io/my-gcp-project/myapp

• Deploy on Google Kubernetes Engine (GKE) by porting your Kubernetes manifests. Some adjustments may be necessary due to differences in storage classes or ingress controllers.

Step 5: Migrate Data and Databases

Data migration is often the trickiest part due to size and consistency requirements.

Using Database Migration Service (DMS)

GCP offers Database Migration Service that supports multiple source types including SQL Server and MySQL on-premises or in other clouds like Azure.

Example workflow:

1. Set up a Cloud SQL instance as the target database.
2. Configure DMS with source connection details pointing at your Azure SQL Database.
3. Initiate continuous data replication minimizing downtime during cut-over.

For Blob Storage Data

Use Storage Transfer Service capable of transferring data directly from Azure Blob Storage buckets into Google Cloud Storage with high throughput and integrity checks.

Step 6: Test Thoroughly Before Cut-over

Before switching production traffic:

• Run full integration tests on all migrated workloads on GCP.
• Validate performance benchmarks against SLAs.
• Check networking connectivity, security groups/firewall rules.
• Perform disaster recovery dry runs.

This step drastically reduces the chances of unexpected issues post-migration.

Step 7: Cut-over and Optimize

With testing validated:

1. Schedule final cut-over during low business hours if possible.
2. Redirect DNS entries / endpoints from Azure resources to GCP equivalents.
3. Monitor application health intensively during initial hours/days.
4. Take advantage of GCP tools like Stackdriver Logging & Monitoring for observability.
5. Review cost reports regularly; utilize recommendations for cost optimizations including rightsizing resources or enabling sustained use discounts.

Bonus Tips for Smooth Migration

• Use Terraform or Deployment Manager scripts for infrastructure as code — makes deployments repeatable and auditable.
• Enable Service Accounts with least privileges for enhanced security posture during migration phases.
• Consider hybrid approaches using Anthos if you want multi-cloud orchestration post-migration.

Final Thoughts

Migrating workloads from Azure to GCP doesn’t have to feel overwhelming or risky. By carefully planning each step—from assessment through testing—you can seamlessly transition while unlocking powerful new cloud capabilities unique to Google Cloud Platform.

Whether you are migrating legacy apps or modern containerized microservices, this guide provides a clear roadmap designed specifically for technical teams craving precision, efficiency, and control in their cloud journey.

If you found this guide useful or want more deep dives on specific migration tools and scripts, feel free to comment below! Happy migrating 🚀