Microsoft Fabric vs Databricks: choosing your data platform

The choice between these platforms is rarely settled by a feature comparison. Both can store, transform and serve enterprise data at scale. The decision is usually about the team you have and the workloads you actually run.

If most of your analytics already lives in the Microsoft estate and your reporting layer is Power BI, the integrated option reduces the number of moving parts. Fewer integrations means fewer places for pipelines to break.

If your workloads are heavy on data engineering and machine learning, and your team is comfortable in notebooks and code, the more engineering-centric platform tends to give better control over cost and performance.

Consider the skills you can hire and retain, not just the skills you have today. A platform your team cannot staff becomes a dependency on a single individual, which is a risk no architecture diagram will show you.

Model the total cost with realistic assumptions. Compute for scheduled transformation, storage growth, development environments and the cost of idle capacity all matter more than list pricing.

Do not underestimate governance. Lineage, access control and the ability to answer where a number came from will determine whether the platform survives its first audit.

In practice many organisations end up running both for a period. That is acceptable if it is a deliberate transition with an end date, and expensive if it is simply the result of two teams making separate decisions.