How a Global Supply Chain Organisation Migrated Hundreds of Data Pipelines to Azure Across 18 Months Without Disrupting Operations

The limitations of an on-premises data environment compound over time. Scaling to handle peak demand requires capital investment and lead time, not a configuration change. Pipelines built without a unified architecture accumulate dependency chains that make any significant change, let alone a full migration, harder to sequence safely. Without centralised monitoring, the signal that something is wrong often arrives after operational impact has already occurred.

The migration itself introduced its own pressure. A supply chain organisation that loses visibility into its data flows during a system transition faces consequences that extend well beyond IT. The business case for moving to cloud was clear. The execution risk was the constraint that had to be managed.

Edstem's starting point was a structured assessment of every system in scope: its business criticality, data size, dependency count, and migration complexity. This produced a sequenced migration roadmap across an 18-24 month programme, with the highest-risk systems handled in phases that protected live operations throughout.

The cloud data platform was built on Microsoft Azure with SingleStore for high-performance data storage, Apache Airflow for pipeline orchestration, and Apache Kafka for data streaming. Python and SQL drove transformation and analytics workloads. CI/CD pipelines through Azure DevOps automated deployment across all platform components, with Azure Key Vault managing secrets and role-based access controls enforcing governance standards. Datadog provided end-to-end infrastructure visibility, with monitoring dashboards and alerts configured so issues could be caught and resolved quickly rather than surfaced through operational failure.

Edstem worked alongside PwC and the client's own architecture teams throughout. The migration progressed domain by domain, with a structured approach to tracking dependencies and milestones that kept the programme on schedule and gave the client clear visibility week by week.

The result was a cloud-native data platform that replaced the legacy on-premises environment without disrupting the supply chain operations it supports. The platform handles both operational and analytical workloads, and its architecture is designed to scale alongside the business without requiring a repeat of the migration effort.