I have worked with customers where I recommended Fabric. I have worked with customers where I recommended a hybrid Databricks/Fabric architecture. And I have worked with customers where I recommended going all-in on Databricks end-to-end.

People sometimes expect consultants to have a single favourite. The reality is more nuanced — and I think being honest about that nuance is more useful than picking a team.

The All-In Databricks Case

There is something genuinely special about a pure, committed Databricks platform. When an organisation goes all-in, they benefit from:

• Deep integration across the full data lifecycle — ingestion, transformation, governance, ML, and serving all within a coherent architecture.
• Open source foundations — Delta Lake, MLflow, Apache Spark. Your data is not locked to proprietary formats.
• A platform that is genuinely pioneering — Unity Catalog, Genie, Mosaic AI. The pace of innovation is remarkable.

The cost of commitment is real — it requires upskilling, cultural buy-in, and proper governance from day one. But organisations that make that investment tend to move faster and with more confidence than those with fragmented tooling.

When Fabric Makes Sense

Microsoft Fabric has closed the gap rapidly, and for many organisations — particularly those deeply invested in the Microsoft ecosystem — it is the pragmatic choice:

• Existing Microsoft licensing — if you are already on M365 and Azure, the Fabric capacity model can be significantly more cost-effective.
• Business user accessibility — Power BI integration and the Fabric interface lower the barrier for non-engineers.
• Near real-time scenarios at approachable scale — Eventstream and Real-Time Intelligence are genuinely good for operational monitoring use cases.

When the Answer Is Both

Hybrid architectures are underrated. A common pattern I have helped customers implement is using Databricks as the core compute and governance layer (Unity Catalog, Delta, Structured Streaming) while using Fabric’s Power BI and semantic layer for business-facing reporting. You get the engineering rigour of Databricks and the accessibility of the Microsoft BI ecosystem.

The risk with hybrid is complexity — two platforms means two sets of skills, two governance models, and two billing relationships. It is worth it when the use cases genuinely require both, and not worth it when it is driven by organisational politics or indecision.

The Framework I Actually Use

When I am helping a customer choose, I ask:

1. What outcomes are you trying to achieve? Technology follows requirements, not the other way around.
2. What does your existing ecosystem look like? Switching costs are real. Build on what you have where it makes sense.
3. Where is your team’s expertise today? The best platform is the one your team can actually operate.
4. What does the next three years look like? Platform choices are hard to reverse. Think beyond the immediate use case.

If you are working through a platform decision and want a second opinion, feel free to reach out on LinkedIn.