Score the workload before the vendor
Define representative sources, volumes, refresh and latency needs, transformation complexity, BI interaction, concurrency, access patterns, recovery expectations, deployment workflow, and support ownership.
Use the same workload contract and acceptance evidence for every candidate so the evaluation does not quietly favor the platform used to define the test.
- Source and integration fit
- Storage and transformation
- Semantic and BI path
- Identity and access
- Workload isolation
- Deployment and recovery
- Observable cost
- Available operating skills
Where Fabric may fit
Fabric deserves serious consideration when Power BI, Microsoft Entra ID, Azure administration, and Microsoft data services already shape the operating environment. Its integrated workloads can reduce handoffs across ingestion, OneLake, warehouse or lakehouse storage, semantic models, and reporting.
The pilot must still validate capacity behavior, workload isolation, regional requirements, deployment, support, and the economics of the intended concurrency and refresh pattern.
Where Snowflake may fit
Snowflake deserves serious consideration when the organization needs a broadly accessible governed data platform, elastic independent compute, cross-tool consumption, controlled data sharing, or an established Snowflake operating practice.
The pilot must validate transformation and orchestration choices, Power BI connectivity and semantic behavior, role design, workload sizing, cost controls, deployment, and internal ownership.
Use a decision record, not a winner slide
The output should state the selected boundary, observed results, unresolved risks, operating responsibilities, estimated run cost, migration implications, and the conditions that would change the decision.
A valid conclusion may be Fabric, Snowflake, coexistence, or improvement of the current environment. Evidence should be allowed to reject the preferred product.
Primary sources
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