Global Automotive Intelligence —
Azure & Data Platform

The Challenge

A global automotive intelligence organisation needed to fundamentally transform how it collected, stored, processed, and distributed market data. Operating across more than 100 markets, the organisation was generating petabyte-scale datasets — but its existing data architecture had not been designed for this volume. On-premises infrastructure was costly to operate and impossible to scale at the pace the business required.

The organisation needed a greenfield enterprise data platform on Azure: one that could ingest, process, and serve petabyte-scale data reliably, support complex analytics for 100+ market datasets, and do so with the security controls, access governance, and cost management that a globally distributed operation demands. At the same time, an estate of more than 750 servers needed to move off on-premises infrastructure and onto Azure PaaS services, with DevOps and container patterns replacing traditional server-based deployment models.

Both workstreams needed to run in parallel — and both needed architectural leadership capable of operating at a global scale.

What We Did

Magma Cloud provided enterprise architecture and delivery leadership across both workstreams.

For the greenfield data platform, we designed and delivered an Azure-native architecture built on:

• Azure Data Lake Storage Gen2 — hierarchical namespace storage providing the scalable, secure foundation for petabyte-scale data ingestion
• Delta Lake — ACID-compliant data lake format enabling reliable, performant analytics on large-scale datasets with schema evolution and time-travel capabilities
• Azure Synapse Analytics — unified analytics workspace integrating data engineering, data warehousing, and machine learning, serving the complex analytical workloads across 100+ market datasets
• Power BI Premium — enterprise reporting and analytics layer, providing consistent, governed data visualisation for business users across the global operation

Security was built into the platform from day one: network segmentation, role-based access controls, sensitivity classification, and audit logging were embedded in the architecture rather than retrofitted after delivery.

For the infrastructure migration, we led the technical architecture and programme governance for the migration of more than 750 servers from on-premises to Azure PaaS services. This involved re-platforming workloads onto container-based deployments, implementing DevOps pipelines for continuous delivery, and rationalising the server estate as workloads moved. The migration significantly reduced the organisation's operational overhead while improving deployment consistency and security posture.

Outcomes

• Greenfield enterprise data platform delivered on Azure, supporting petabyte-scale datasets across 100+ global markets
• Data Lake Gen2, Delta Lake, Synapse Analytics, and Power BI Premium architecture operational in production
• 750+ servers migrated to Azure PaaS with DevOps and container patterns, reducing operational overhead and infrastructure debt
• Security controls embedded throughout — network segmentation, RBAC, sensitivity classification, and audit logging from day one
• Platform capable of scaling to support the organisation's continued global growth without re-architecture

Services Used