The Challenge
Where things stood before
The client operated 20+ disparate data sources across Azure, AWS and on-premises SQL Server. Analytics workloads were slow (30–60 min queries), scanning terabytes for every dashboard refresh. Compute costs were spiraling and there was no unified governance layer.
Our Approach
How we engineered the outcome
- Assessed all 20+ source systems and mapped a unification blueprint to Microsoft Fabric OneLake.
- Implemented adaptive Liquid Clustering on high-cardinality columns to accelerate filter pushdown.
- Added incremental statistics collection and predicate pushdown across gold-layer tables.
- Refactored PySpark transformations for parallel processing and skew mitigation.
- Built metadata-driven ingestion pipelines to standardize onboarding for new sources.
The Outcomes
Measurable business impact
82%
Faster query execution
97%
Less data scanned
83%
Lower compute consumption
20+
Sources unified into OneLake
