Data Platform & Cloud Architecture
Over 60% less time preparing data
An enterprise organization
60%+
less time spent preparing data
01 / Challenge
The problem.
Data was spread across siloed warehouses, inconsistent pipelines, and manual ETL. The company needed a shared foundation for analytics and AI.
02 / Approach
How we built it.
We architected a full lakehouse on Microsoft Fabric that unifies structured and unstructured data, then migrated the legacy warehouses onto it. It ships with automated ingestion, a semantic layer for self-service, role-based governance, and hooks for ML training and deployment.
03 / What we built
The system.
01
Data lakehouse on Microsoft Fabric
02
Automated ingestion pipelines
03
Semantic layer for self-service analytics
04
Role-based governance
05
Legacy warehouse migration
06
Real-time streaming for operational use cases
04 / Outcomes
What changed.
Teams run analytics and AI against a single source of truth
Governance went from manual and inconsistent to automated and auditable
One platform covers the path from raw data to production AI models