Talk to an Expert
04.05.2026
6 min
Migrating a Mission-Critical Data Platform to a Modern Lakehouse Architecture
Migrating a mission-critical data platform of this scale—without downtime, without disrupting reporting, and without forcing significant changes on business users—requires deep architectural expertise and precise coordination.
A large international grocery retail organization has taken a major step toward modernizing its data infrastructure by completing the first phase of a full-scale migration from a legacy corporate data warehouse to a modern lakehouse architecture. The transformation focused on replacing a Microsoft-based stack with a flexible, vendor-independent platform built on open source technologies.
A large international grocery retail organization has taken a major step toward modernizing its data infrastructure by completing the first phase of a full-scale migration from a legacy corporate data warehouse to a modern lakehouse architecture. The transformation focused on replacing a Microsoft-based stack with a flexible, vendor-independent platform built on open source technologies.
Challenge
The key challenge was not just modernization — it was a full-scale migration of a live, mission-critical system:
At the heart of the new architecture are Apache Spark, Trino, MinIO, and Apache Airflow—a combination that reflects a broader industry shift toward scalable, cloud-agnostic data platforms.
The migration was driven by rapidly growing data volumes and increasing analytical demands. At the start of the project, the retailer’s data warehouse had already reached 11 TB, with an annual growth rate of around 20%. Infrastructure utilization was nearing its limits, consistently operating at 85–90% capacity. At the same time, the existing architecture imposed constraints on scaling and flexibility, while reliance on proprietary technologies introduced additional operational and external risks.
Rather than opting for a disruptive “big bang” replacement, the retailer implemented a hybrid migration strategy. The legacy system and the new lakehouse platform were run in parallel, allowing data pipelines and analytical layers to be transferred incrementally. This approach ensured that business operations continued uninterrupted, with no impact on reporting or day-to-day decision-making.
During the first phase, the team successfully migrated core data ingestion processes and key sales data marts to the new platform. The solution was fully integrated with existing enterprise systems, including ERP and point-of-sale infrastructure, as well as the legacy data warehouse. This careful orchestration allowed the organization to preserve continuity while gradually shifting to a more advanced architecture.
- Replace a legacy proprietary stack with open source technologies
- Maintain full business continuity during migration
- Preserve existing reporting and analytics
- Integrate seamlessly with ERP systems, POS systems, and legacy storage
- Enable scalability without redesigning the architecture in the future
At the heart of the new architecture are Apache Spark, Trino, MinIO, and Apache Airflow—a combination that reflects a broader industry shift toward scalable, cloud-agnostic data platforms.
The migration was driven by rapidly growing data volumes and increasing analytical demands. At the start of the project, the retailer’s data warehouse had already reached 11 TB, with an annual growth rate of around 20%. Infrastructure utilization was nearing its limits, consistently operating at 85–90% capacity. At the same time, the existing architecture imposed constraints on scaling and flexibility, while reliance on proprietary technologies introduced additional operational and external risks.
Rather than opting for a disruptive “big bang” replacement, the retailer implemented a hybrid migration strategy. The legacy system and the new lakehouse platform were run in parallel, allowing data pipelines and analytical layers to be transferred incrementally. This approach ensured that business operations continued uninterrupted, with no impact on reporting or day-to-day decision-making.
During the first phase, the team successfully migrated core data ingestion processes and key sales data marts to the new platform. The solution was fully integrated with existing enterprise systems, including ERP and point-of-sale infrastructure, as well as the legacy data warehouse. This careful orchestration allowed the organization to preserve continuity while gradually shifting to a more advanced architecture.
Key Value Delivered
The new platform is built around a structured, layered data model which provides transparency and control across the data lifecycle. Automation also plays a central role: ETL processes are generated through a framework that accelerates development and simplifies onboarding of new data sources. At the same time, role-based access control and resource management mechanisms ensure secure and efficient usage across teams.
The transition to a lakehouse model brings immediate benefits. The platform is now horizontally scalable, capable of handling growing workloads without architectural redesign. The use of open source technologies eliminates vendor lock-in and reduces total cost of ownership. Analysts gain more flexibility through SQL-based access and self-service capabilities, while engineering teams benefit from faster development cycles.
1. Zero-downtime migration
A hybrid approach allowed the system to evolve without interrupting business processes.
2. Scalability by design
The platform supports growing data volumes and workloads without architectural changes.
3. Vendor independence
Transition to open source eliminated vendor lock-in and reduced external risks.
4. Faster time-to-market
Automation significantly ускорила подключение новых источников данных и создание витрин.
5. Cost optimization
Object storage and license-free technologies lowered total cost of ownership (TCO).
6. Empowered analytics
Self-service capabilities and SQL access improved productivity for analysts.
The transition to a lakehouse model brings immediate benefits. The platform is now horizontally scalable, capable of handling growing workloads without architectural redesign. The use of open source technologies eliminates vendor lock-in and reduces total cost of ownership. Analysts gain more flexibility through SQL-based access and self-service capabilities, while engineering teams benefit from faster development cycles.
1. Zero-downtime migration
A hybrid approach allowed the system to evolve without interrupting business processes.
2. Scalability by design
The platform supports growing data volumes and workloads without architectural changes.
3. Vendor independence
Transition to open source eliminated vendor lock-in and reduced external risks.
4. Faster time-to-market
Automation significantly ускорила подключение новых источников данных и создание витрин.
5. Cost optimization
Object storage and license-free technologies lowered total cost of ownership (TCO).
6. Empowered analytics
Self-service capabilities and SQL access improved productivity for analysts.
What’s Next
The first phase of the project has laid a solid foundation for future growth, enabling the organization to expand its analytical capabilities and adapt to evolving business requirements.
This case highlights a key capability: executing complex, enterprise-grade data platform migrations across fundamentally different technology stacks—safely, incrementally, and with zero disruption to the business.
The platform is now fully prepared to evolve alongside business growth — without technological limitations.
This case highlights a key capability: executing complex, enterprise-grade data platform migrations across fundamentally different technology stacks—safely, incrementally, and with zero disruption to the business.
The platform is now fully prepared to evolve alongside business growth — without technological limitations.
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