Data Platform · Cloud Migration
Migrating a Global Enterprise to a Governed Lakehouse
How a fragmented, multi-region data estate became a single governed lakehouse — and what it took to get there without breaking the business.
Problem
Reporting was slow, expensive, and contradictory. Each region maintained its own extracts and definitions, so the same metric could return three different answers depending on who you asked.
Business Context
Leadership needed a single, trustworthy view of the business to make capital and risk decisions. Regulatory reporting demanded auditable lineage that the existing patchwork simply could not provide.
Architecture
We designed a cloud lakehouse on a medallion architecture — bronze for raw landing, silver for conformed and validated data, gold for business-ready marts. Snowflake served as the warehouse layer, with dbt for transformation and a metadata catalogue providing lineage across every hop.
Technical Decisions
We chose config-driven ingestion over bespoke pipelines to keep onboarding cheap, invested early in a semantic layer to lock down definitions, and made data quality gates a hard requirement for promotion between medallion layers.
Trade-offs
A single conformed model slows down the first few domains but pays off exponentially as the platform grows. We accepted slower initial delivery in exchange for consistency and governance that would compound over years.
Implementation
We migrated domain by domain, running old and new in parallel and reconciling outputs until the business trusted the new numbers. Each cutover was reversible until sign-off.
Challenges
The hardest part wasn't technology — it was reconciling conflicting definitions across regions. We ran a definitions working group to converge on a single source of truth before writing the models.
Performance Improvements
Warehouse tuning, clustering, and query rewrites cut both latency and cost. Curated gold marts meant dashboards queried pre-aggregated data rather than scanning raw tables.
Business Value
The organisation gained one governed source of truth, faster onboarding of new data, and auditable lineage for regulators — turning the data platform from a cost centre into a decision-making advantage.
Future Enhancements
Layering agentic AI on top: automated data quality remediation, LLM-generated documentation, and natural-language access to the governed semantic layer.
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