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Microsoft Fabric or your own data warehouse: which is wiser?
Fabric if you are already on Microsoft and want to move quickly without managing infrastructure. Your own setup on Azure if you need control over cost and components, or have pipelines you will not rewrite. The choice matters less than keeping raw, cleaned and modelled data in separate layers, because that is what makes the decision reversible.
The comparison, honestly
| Microsoft Fabric | Own setup on Azure | |
|---|---|---|
| Speed to first result | Fast, little to set up | Slower, more choices to make |
| Cost model | Fixed capacity per month | Per component, more variable |
| Control | Limited to what Fabric offers | Full, including the parts you did not want |
| Who maintains it | Largely Microsoft | You, or us |
The three layers, whichever you pick
- Raw. Exactly as it came out of the source, untouched, so you can always go back.
- Cleaned. Types corrected, duplicates resolved, keys made reliable.
- Modelled. The star schema the reports read from.
Keep those apart and switching platform is a migration. Blur them and it is a rebuild. That is the decision that outlives the product names.
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Book half an hourFrequently asked questions
Microsoft Fabric or your own data warehouse?
Fabric if you are already on Microsoft and want to move quickly without managing infrastructure. Your own setup on Azure if you need control over cost and components, or if you have existing pipelines you are not going to rewrite. The choice matters less than keeping the layers separate, because that is what lets you switch later.
What does Fabric actually cost?
It runs on capacity, and capacity is a fixed monthly amount rather than a price per query. That is predictable but it also means you pay while nothing is running, unless you pause it. Work out your busiest month rather than your average one.
Can you start small and grow?
Yes, and that is what we recommend. Start with one subject and one source, get the model right, then add. A platform built for everything before anything works tends to be finished exactly when the requirements have changed.
What about our existing SQL Server?
It often stays. A gateway or a replication into the cloud layer is usually enough, and rewriting a working warehouse rarely pays for itself. What changes is where the modelled layer lives, not where the data is born.
How do you keep the choice reversible?
By keeping raw, cleaned and modelled data in separate layers, and by keeping definitions outside the report and outside the pipeline code. Then switching platform is a migration, not a rebuild.