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Databricks or Microsoft Fabric: why we build on Databricks
We build data platforms on Databricks, and this guide shows why. Databricks and Microsoft Fabric cover a lot of the same ground, and both can sit on Azure. The table sets out the criteria that decide: who builds on the platform, what runs on it and how it is governed. Prices and benchmark results are out of scope.
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Criteria
| Criterion | Databricks | Microsoft Fabric |
|---|---|---|
| Starting point | Built around data engineering, data science and machine learning on a lakehouse. Code-first teams feel at home. | Built as one Microsoft analytics service with Power BI at its centre. Analyst-led teams feel at home. |
| Main users | Engineers and data scientists, with SQL and BI on top. | Analysts and BI developers, with engineering features beside them. |
| AI and machine learning | Machine learning and AI workloads are a core part of the platform. | Data science and AI features are part of the service. Check that they cover your specific workload. |
| Fit with Microsoft 365 and Power BI | Works with Power BI and Azure services as a separate platform you integrate. | Integrated by design with Power BI, OneLake and Microsoft sign-in and administration. |
| Governance | Unity Catalog governs data and models across workspaces. | Governance sits with Fabric workspaces, OneLake security and Microsoft Purview. Compare how each maps to your access model. |
| Open formats and exit | Delta Lake tables are the main format, and other engines can read them. | OneLake also stores tables in Delta format. In both cases, test how data moves out before you rely on it. |
| Clouds | Available on more than one cloud. | A Microsoft service that runs on Azure. |
| Delivery as code | Infrastructure and jobs as code, with Terraform and Asset Bundles, are well established. | Git integration, deployment pipelines and APIs exist. Check how far code-based delivery reaches for the items you plan to use. |
| Operating model | You choose and tune compute: more control, more to manage. | A managed capacity model: fewer settings to manage, and less control over them. |
| Cost model | Consumption based. Cost follows how you configure and schedule workloads. | Capacity based. Cost follows the capacity you reserve and how shared workloads use it. Compare on your own workload, not on list prices. |
Why Databricks
What this means for your platform
- Engineers, analysts and data scientists work on one platform, from pipelines to machine learning and AI.
- Power BI reads from Databricks through SQL warehouses, so BI users keep the tools they know.
- Unity Catalog governs data, models and access in one place, with reader and contributor groups per schema.
- Delta and Iceberg keep your data in open formats, so the platform stays yours.
- Terraform and Asset Bundles deliver the platform as code, from dev to production.
Already on Fabric or Power BI?
- Power BI and your existing Microsoft tools connect to a Databricks platform.
- We map your current reports and datasets before anything moves.
- We plan the migration of workloads to Databricks in steps, with your reports kept running.
- Sign-in and access stay with Microsoft Entra ID.
Our position
NexOps builds data platforms on Databricks on Azure. Our references and credentials are all on Databricks, with Power BI on top for reporting.
What to check first
- List who will build on the platform and who will only consume from it.
- Write down the workloads of the next twelve months: reporting, machine learning and AI.
- Map your identity and access model to Unity Catalog groups and schemas.
- Check the skills you have, and the skills you would have to hire or train.
- Move one real table out of Delta to confirm open formats and exit.
- Run one real workload and model cost from your own usage, not from list prices.
Next step
Facing this decision?
Tell us about your situation. We will say which side we would lean towards and what we would test first.
Currently accepting projects for Q4 2026