Industry:
Marketing Data Services
Company Size:
Marketing Data Services
Location
Philippines
A mid-sized marketing data services company approached CDOps Tech to modernize their infrastructure and accelerate innovation.
Their platform was running on IBM Cloud, supporting critical data processing workloads. While the environment was stable, it was becoming increasingly difficult to scale and adopt modern cloud capabilities.
Leadership wanted to move to a more flexible and innovation-driven platform, and selected Google Cloud Platform (GCP).
The Challenge
As the business grew, the limitations of the existing environment became clear.
The team faced several obstacles:
- Rising operational costs and licensing complexity on IBM Cloud
- Limited access to modern cloud-native services for AI and data analytics
- Difficulty integrating with SaaS platforms and other cloud environments
- Slower innovation cycles compared to competitors using modern data platforms
Our Solution
We led the end-to-end cloud migration using a structured migration framework designed for data-intensive environments.
The approach focused on minimizing risk while modernizing the architecture.
1. Assessment & Planning
We began with a comprehensive evaluation of the client’s infrastructure.
This included:
- Application portfolio and dependency analysis
- Infrastructure and licensing review
- Cost comparison between IBM Cloud and GCP
- Designing a secure GCP Landing Zone with IAM, networking, and compliance baselines
This stage ensured the migration plan aligned with both technical requirements and business goals.
2. Architecture Design
Next, we redesigned the platform architecture to take advantage of Google Cloud’s modern services.
Key architectural improvements included:
- Migrating containerized workloads to Google Kubernetes Engine (GKE)
- Deploying lightweight services using Cloud Run
- Designing secure VPC and IAM structures aligned with enterprise best practices
- Creating integration pipelines with existing SaaS platforms such as Salesforce and Workday using Apigee and Pub/Sub
This architecture enabled the platform to become cloud-native and multi-cloud ready.
3. Migration Execution
Once the architecture was validated, we executed the migration in phases.
Key migration activities included:
- Lift-and-shift migration of stable workloads from IBM VMs to Google Compute Engine
- Re-platforming legacy Java applications into containerized workloads on GKE Autopilot
- Migrating databases from IBM Cloud to BigQuery and Cloud SQL
- Implementing CI/CD pipelines using Cloud Build and Artifact Registry
This approach allowed the team to migrate safely while gradually modernizing the platform.
4. Optimization & Handover
After migration, we focused on ensuring the environment was secure, observable, and cost-efficient.
Key improvements included:
- FinOps implementation to monitor and optimize cloud spend
- Platform observability using Cloud Monitoring and Error Reporting
- Security hardening through Security Command Center and DLP APIs
Finally, we conducted knowledge transfer sessions so the internal team could confidently manage the new environment.
The Results
| Metric | Before (IBM Cloud) | After (GCP) |
|---|---|---|
| Infra Cost Reduction | - | 28% Savings |
| Deployment Time | Weeks | Minutes via CI/CD |
| Analytics Performance | Sluggish | 5x Faster with BigQuery |
| Innovation Readiness | Limited | Enabled AI/ML Adoption |
| Compliance Alignment | Fragmented | Streamlined with GCP Blueprints |
Client Testimonial
Key Takeaway
Many companies remain locked into legacy cloud environments that limit innovation and increase operational costs.
By migrating to Google Cloud and modernizing the architecture, this client gained a scalable data platform that supports faster development, advanced analytics, and long-term growth.
