Engineering Excellence: How a Tech Leader Migrated to AWS with Zero Business Downtime
This case study explores how a global technology leader successfully executed a GCP to AWS migration with zero downtime. Learn the architecture, tools, and strategy used to migrate petabyte-scale data and customer-facing applications to AWS.
A global leader in technology solutions approached AWS and Quantiphi with a mission-critical mandate: consolidating its dual-cloud architecture. With a technology stack fragmented across both Google Cloud Platform (GCP) and Amazon Web Services (AWS), the company needed to consolidate 10 core customer-facing applications spanning across 3 GCP regions onto the AWS ecosystem.
Serving organizations across diverse industries carries significant compliance responsibilities. The business objective was absolute: migrate the entire technology estate to ensure regulatory alignment with zero downtime for more than 1000 customers.
The migration was built on two uncompromising pillars:
- Future-Ready Scalability: Engineering an architecture capable of supporting the next 1,000+ customers without incremental complexity.
- Zero-Friction Transition: Ensuring a seamless front-end experience where customers remained unaffected by the massive backend shift.
The scale of this transition made it one of the most complex cloud-to-cloud migrations in recent memory. The inventory included over 1,000 BigQuery projects and petabytes of customer data distributed across three global regions.
Migration Overview
- Source Cloud: Google Cloud Platform (GCP)
- Target Cloud: Amazon Web Services (AWS)
- Applications Migrated: 10
- Data Volume: Petabyte-scale
- Regions: 3
- Downtime: Zero
- Key Technologies: Apache Iceberg, EMR, EKS, Snowball, Looker, Prometheus
The Framework: Quantiphi’s Qatapult Modernization and Migration Factory in Action
Quantiphi didn’t approach this migration as a simple lift-and-shift exercise. Instead, the team deployed its proprietary solution Qatapult, an agentic AI-powered modernization and migration hub purpose-built for enterprises moving between cloud providers.
Qatapult is available on the AWS Marketplace and is designed as a factory-driven solution that delivers approximately 30% faster workload migration and roughly 40% process automation through AI-powered microsolutions. Qatapult’s phased engagement model moves through four distinct stages including Planning, POC, Shared Services, and Migration, ensuring that modernization is driven systematically across the application, data, and infrastructure layers. What made this engagement stand apart, however, was how Quantiphi wove together three powerful accelerators throughout every phase: Apache Iceberg as the modern data foundation, GenAI for code conversion, refactoring and testing, and agile, factory-driven execution to meet the deadline.
Step-by-Step GCP to AWS Migration Process
Phase 1: Planning — Discovery, Blueprint, and Rapid Mobilization
The engagement began with a comprehensive assessment of the entire GCP and AWS landscape. Quantiphi’s team initiated inventory of every application, data pipeline, infrastructure component, and observability tool. Key planning activities included:
- Existing Landscape documentation: Quantiphi’s GenAI tool, Codeaira, drafted high-level landscape documentation and the end-state architecture, accelerating a planning exercise that traditionally takes weeks.
- Track and wave identification: Migration patterns were organized by workload type, complexity, and inter-service dependencies, laying the groundwork for the parallel, agile execution model that would follow
- Target tech stack mapping:Quantiphi’s architects mapped each GCP service to its most appropriate AWS equivalent, informed by both technical feasibility and cost optimization
- Apache Iceberg selection: For the massive BigQuery data estate, the team recognized early that a modern open table format would be essential; Iceberg emerged as the leading candidate due to its support for ACID transactions, schema evolution, and partition evolution capabilities that were non-negotiable for a technology platform processing over millions of incremental records daily across three global regions
To deliver the engagement in less than a year, Quantiphi ramped up the delivery team to more than 50 engineers across the United States, Canada and India. This global distribution enabled near-round-the-clock development coverage and allowed the team to work alongside the customer’s distributed product teams from day one.
Phase 2: POC — Validating Design Patterns with Evidence, Not Assumptions
Before committing to any target technology at scale, Quantiphi conducted multiple proofs of concepts, a discipline that ultimately saved the project from costly rework. The BigQuery migration alone involved testing Redshift and Athena as potential targets, with each evaluated for query performance, cost, and operational characteristics. The team even redesigned the Redshift schema to mitigate feature differences between BigQuery and Redshift.
Ultimately, Amazon Athena with Apache Iceberg tables proved to be the best fit. Iceberg’s time-travel capability, the ability to query historical snapshots of data, and its partition-evolution features enable the customer to maintain full analytical flexibility without the rigid schema constraints of traditional data warehouses. Query latency improved by approximately 15% compared to the previous BigQuery setup, validating the architecture choice with hard performance data.
Alongside the data layer evaluation, multiple service mappings were validated through POCs:
- Data Processing: Migrating Dataflow pipelines to AWS-native processing services
- Data Transfer: Utilizing AWS Snowball to migrate data to S3 at petabyte scale
- Format Conversion: Developing ETL jobs developed on Amazon EMR to convert Parquet format data to Iceberg format for storage in target tables
Phase 3: Shared Services — Building the Foundation with AI-Powered Automation
With design patterns validated, the team built a common foundation on AWS and it was here that GenAI powered tooling delivered the most impressive gains.
Terraform modules for AWS managed services were refactored from their GCP equivalents using AI-assisted code generation through Quantiphi’s proprietary tools, including Codeaira, our GenAI-powered developer assistant. Rather than manually rewriting hundreds of infrastructure definitions, AI generated the initial conversions, which engineers then reviewed and validated. AI automation delivered a 10% reduction in infrastructure provisioning time, a meaningful gain given the deployment spanned four regions.
The broader shared services layer was configured and deployed as the backbone upon which all subsequent migration tracks would operate:
- Observability: Prometheus
- DevOps: GitHub and Artifactory
- CI/CD: Jenkins for pipeline orchestration
Phase 4: Migration — Agile Execution at Massive Scale
With the foundation in place, the migration entered its most intensive phase. Rather than migrating sequentially, the team organized work into parallel tracks by workload type, complexity, and dependency chains, a hallmark of Quantiphi’s factory-driven, agile approach. Track 1 handled container migration, while Track 2 focused on data pipeline modernization and Track N addressed visualization and reporting. Customer application teams were split into groups sharing similar design patterns, enabling the reuse of IaC, ETL job conversion and historical data migration scripts across multiple teams drastically reducing development time.
On the application front, GKE clusters were migrated to Amazon EKS. GenAI-assisted analysis identified configuration differences between GKE and EKS, flagged potential compatibility issues, and generated EKS-compatible manifests, resulting in 25% faster container orchestration setup. Applications were refactored to improve performance and reduce costs by right-sizing infrastructure, with management handled via Rancher and autoscaling powered by Karpenter.
The data migration was where Apache Iceberg truly proved its worth at scale. The CDC pipelines designed during the POC phase were deployed across the full estate, delivering remarkable results:
- More than 1,000 BigQuery projects were migrated to set up over 5,000 Iceberg tables on AWS.
- Petabytes of customer data were handled with automated incremental ingestion workflows
- Millions of incremental records were processed daily across three regions
- ACID transaction support ensured data integrity even as production systems continued writing new data in real time
- A minute-level data refresh was achieved across all dashboards, meeting a 99.9% SLA
This proved that the Iceberg-based architecture delivered not just at migration time but as a production-grade analytical foundation.
Modernizing the ETL jobs from GCP-native services (Dataflow, BigQuery) to AWS-native equivalents (EMR) required substantial code rewriting. GenAI accelerated this refactoring by 25%, generating boilerplate conversion code and identifying patterns across similar pipeline architectures. Meanwhile, more than 20 dashboards were migrated from GCP Looker to Looker on AWS to ensure a consistent customer experience, the same interface, the same data freshness, and the same reliability.
Quantiphi established a robust governance structure with the customer’s leadership team throughout this phase, tracking key project goals and proactively identifying deviations. Regular checkpoints ensured that cost-intensive migrations remained on schedule, and any emerging risks were surfaced and addressed before they could impact the non-negotiable regulatory deadline
Technologies Used
Amazon Athena
Apache Iceberg
Amazon EMR
AWS Snowball
Amazon EKS
Terraform
The Results: Quantifiable Business Impact
The migration delivered outcomes that validated the investment and approach across every dimension.
Business Impact:
- 30% reduction in cloud operational costs, contributing to the customer’s profitability goals
- Scalable infrastructure designed to support the next wave of 1000+ customers without architectural rework
Technical Impact (GenAI Value):
- 10% reduction in infrastructure provisioning time
- 25% faster ETL code refactoring
- 25% faster container orchestration setup
- 20% boost in time for setting up monitoring systems
Key Takeaways for Enterprises Planning Cloud-to-Cloud Migrations
This case study offers a roadmap for global organizations navigating the complexities of large-scale cloud consolidation. It proves that with the right partnership and a disciplined architecture, organizations can modernize vast technology estates without compromising on compliance or customer experience.
- Invest in a structured methodology. Quantiphi’s phased migration framework, planning, POC, Shared Services, and Migration, provided a repeatable, scalable approach that kept a massively complex project on track. Without a structured methodology, large migrations tend to fragment into disconnected workstreams, leading to inconsistent outcomes.
- Let data drive technology decisions. The decision to use Apache Iceberg on Athena over Redshift emerged from rigorous POC testing, not from assumptions or vendor preferences.
- Use GenAI as an accelerator, not a replacement. AI-assisted code generation, infrastructure provisioning, and query conversion delivered measurable productivity gains. But the engagement also demonstrated that clear coding standards, precise context, and human validation remain essential for quality outcomes.
- Plan for the factory, not the project. By organizing customer application teams into groups with similar design patterns and building reusable implementation templates, Quantiphi turned a one-off migration into a repeatable, scalable operation. This factory-driven approach was the key enabler for meeting an aggressive timeline with a rapidly scaled team.
- Governance is not optional. For migrations driven by compliance requirements, strong governance structures with executive sponsorship are essential for maintaining velocity and surfacing risks early.
Conclusion
The GCP to AWS migration of a global technology leader showcases a masterclass in cloud-to-cloud transformation. By weaving together the Qatapult platform’s phased migration approach, Apache Iceberg as a modern data foundation, GenAI-powered automation at every layer, and agile factory-driven execution, Quantiphi delivered a migration that was faster, more cost-effective, and more reliable than conventional approaches.
To learn more about Quantiphi’s cloud migration capabilities and the Qatapult platform, visit Use the link to our partner page: https://quantiphi.com/partners/amazon-web-services/
