Platform
Data Platform, built to run.
Production-grade Databricks Lakehouse on Azure, engineered for regulated environments and high-volume analytics.
Problem
The problem
Most enterprise data platforms fail the same way: they look fine in the demo, and then buckle when real load arrives or when the regulator asks a question that wasn’t in the original requirements. The gap is rarely at the storage layer. It’s in the architecture — what lives where, who owns what, and whether governance was designed in or bolted on.
Delivery
What the practice delivers
The Cloudbuilder Data Platform is a production-grade Databricks Lakehouse on Azure — Delta Lake for storage, Spark and SQL for compute, CI/CD pipelines for delivery, and identity, security and governance designed in from day one. The practice is stack-agnostic across Azure and Databricks: the same team ships Databricks Lakehouse, Azure-native pipelines, and Kimball-modelled warehouses on the engagements that require them.
Microsoft Fabric implementation is in active development within the practice; it is not yet a client-facing delivery option.
Track record
In practice
The Data Platform has been delivered into regulated financial services, high-volume marketplace analytics, and aviation infrastructure modernisation. Each engagement designs architecture against the specific compliance surface, the specific scale, and the specific handover target — not against a template. Principal-led throughout.
Architecture detailRead moreHide
The Lakehouse uses a layered medallion pattern — raw ingest, validated, business-ready — with Delta Lake as the storage substrate. Spark and SQL serve compute against the same tables; CI/CD pipelines deploy schema changes, transformation logic, and access policies through environment promotion gates. Every change is reviewed before it reaches a production layer; rollback is a deployment, not a recovery.
Where the engagement calls for it, the same architecture extends to Azure-native pipelines (Data Factory, Synapse warehouses) or to Kimball-modelled dimensional layers built on top of the Lakehouse. The decision is shaped by the compliance surface and the existing estate — not a default.
Governance designed-in, not bolted-onRead moreHide
Identity, access, and lineage are part of the platform’s architecture, not a layer added afterwards. Access controls are environment-scoped and role-based, with the access model versioned alongside the data model. Lineage is captured as data flows through the medallion layers, so the path from a reporting figure back to source records is reconstructable without manual reverse engineering.
Environment isolation is enforced at the workspace and permission layers; production data does not leak into development surfaces by accident. Audit logs capture who saw what, when, and what changed — the questions a regulator asks land against a governance surface that already holds the answers.