The Challenge
Where things stood before
Fabric Warehouse queries were scanning terabytes for every dashboard refresh. Analysts abandoned reports mid-run and compute credits were burning through the F64 capacity daily.
Our Approach
How we engineered the outcome
- Profiled top-50 Warehouse queries via Fabric Capacity Metrics and identified clustering gaps.
- Rolled out adaptive Liquid Clustering on high-cardinality join keys.
- Added incremental statistics collection and predicate pushdown across gold-layer tables.
- Refactored PySpark transformations with skew handling and broadcast tuning.
- Migrated legacy T-SQL transforms to dbt-fabric models with tests and docs.
The Outcomes
Measurable business impact
83%
Lower compute consumption
97%
Less data scanned
60–70%
Analytics performance gain
F64→F32
Capacity right-sized
