Fabric or Databricks?
Six questions. No fence-sitting. A leaning verdict with the tradeoff named out loud — because "it depends" isn't an answer.
Build for either · pick one · greyskullanalytics.com
The criteria, in plain terms
The same six factors that drive the verdict — for when you want the why, not just the what.
Fabric leans here when…
The shop already lives in Power BI, M365 and Purview, and the team is analyst-heavy rather than engineer-heavy. Fabric meets them where they are.
Databricks leans here when…
There's an existing Spark/notebook culture, real data engineers, and a team comfortable owning clusters, Unity Catalog and CI/CD properly.
Fabric: capacity
Fixed monthly F-SKU — predictable, easy to budget, often 30–50% cheaper for Microsoft-aligned shops. Watch the Power BI licensing cost hiding underneath the SKU.
Databricks: consumption
Pay-per-DBU plus compute. Variable, and can win decisively for spiky or compute-heavy ML workloads — but needs governing or it drifts.
Fabric
Strong when the work is modelling, semantic layers and BI delivery — the analytics-engineering end. Low-code paths keep mixed-skill teams moving.
Databricks
Strong when the work is heavy pipelines, streaming, complex transformation and ML — the data-engineering end, where control matters.
Fabric
Direct Lake gives sub-second Power BI over the lake with no import/refresh. If Power BI is the destination, this is a genuine gravitational pull toward Fabric.
Databricks
Serves Power BI perfectly well, plus AI/BI Genie for NL analytics. But you don't get the native Direct Lake intimacy — it's a connected source, not the same fabric.
Fabric
Open Delta underneath, but the experience is SaaS and Microsoft-shaped. Terraform exists but isn't under Microsoft support — IaC is thinner.
Databricks
Open-source heritage, Iceberg, a mature and battle-tested Terraform provider, portable code. The stronger hand if avoiding lock-in genuinely matters.
Fabric
Comfortable for most enterprise BI and moderate engineering. Capacity model can throttle if you push extreme, bursty, or very large parallel workloads.
Databricks
The more mature ceiling for terabyte-scale transformation, ML at scale and demanding concurrency. Built for the heavy end.