Choosing a Microsoft data platform deliberately

Platform selection in the Microsoft data ecosystem is easier than it was, and that creates a different risk: choosing by default rather than by fit. The questions below usually separate the cases quickly.

Questions that decide it

1. Who builds and maintains the pipelines: analytics engineers comfortable with code, or a mixed team including analysts?
2. What are the latency requirements in practice, as opposed to as stated? Genuine streaming requirements are less common than requested.
3. How much existing investment exists in notebooks, orchestration and pipelines, and what would migrating it cost?
4. How is capacity to be governed and charged back across business units?
5. What are the data residency, sovereignty and isolation constraints?

Where Fabric fits well

• Organisations standardising on Power BI where a unified semantic layer is the primary goal.
• Mixed teams that benefit from a single environment covering ingestion, transformation, storage and reporting.
• Estates where the main problem is fragmentation of definitions rather than raw processing scale.

Where a specialist platform still earns its place

• Heavy data engineering and machine learning workloads with mature notebook practice.
• Very large-scale processing with cost profiles that reward fine-grained cluster control.
• Substantial existing investment in a working platform where migration cost exceeds the consolidation benefit.

Whatever the platform, the semantic layer decides trust

Consolidating storage does not by itself produce agreed numbers. Shared definitions, named owners and a change process for measures are what stop the reconciliation meetings, and they are required on any platform.