Choose Microsoft Fabric if your organisation runs on Microsoft, your main output is Power BI reporting, and you want one product with one bill. Choose Databricks if data engineering and machine learning are central, you need fine control over compute, or you work across more than one cloud. Many Azure customers use both, with Fabric in front of Databricks.
Microsoft Fabric vs Databricks at a glance
| Microsoft Fabric | Databricks | |
|---|---|---|
| What it is | A single analytics suite, delivered as a service | A data and AI platform built around the lakehouse |
| Includes | Data Factory, lakehouse, warehouse, real-time analytics, Power BI | Notebooks, jobs, SQL warehouse, ML and model serving, governance |
| Storage | OneLake, using open Delta tables | Delta tables in your own cloud storage |
| Billing | Capacity you reserve, shared by all workloads | Usage, by the compute each workload consumes |
| Clouds | Azure | AWS, Azure, Google Cloud |
| Strongest at | Reporting and self-service for business users | Engineering depth, scale, machine learning |
They share more than the marketing suggests. Both store data as Delta tables, and Fabric can read Databricks tables through shortcuts without copying them. That shared format is why using the two together is practical.
What Fabric is good at
Fabric's argument is consolidation. One product covers ingestion, storage, modelling and reporting, with one security model and one capacity to pay for. If you've been stitching five Azure services together, that's appealing.
It's at its best when Power BI is where the value lands. Reports read straight from OneLake, and people who already know the Microsoft tools can build without waiting for an engineer.
Capacity billing suits finance teams too. You buy a size and the cost is known in advance. The other side of that coin: when a heavy job eats the capacity, everyone's reports slow down, and you're paying for the capacity whether it's busy or idle.
What Databricks is good at
Databricks gives engineers more room. Compute is chosen per workload, jobs are isolated from each other, and there's a mature toolset for testing, deploying and monitoring pipelines as software.
For machine learning it's well ahead. Experiment tracking, a model registry and serving are part of the platform, which is why most serious data science work on Azure still lands there.
It's also not tied to one cloud. If you have data on AWS as well, or might, the same platform and the same skills apply. Fabric keeps you on Azure.
You pay for that with complexity. Databricks asks for people who understand clusters, cost and job design. It rewards a real data engineering team and punishes the absence of one.
Using Fabric and Databricks together
On Azure this is common and it isn't a compromise. Engineers build and govern the data in Databricks. Fabric points at those tables through OneLake shortcuts and serves them to Power BI.
Each product does what it's best at, and the data is stored once. The thing to settle early is governance: decide which platform owns access rules, or you'll maintain two sets that drift apart.
How to choose
- Where does the value show up? If it's dashboards for business users, lean to Fabric. If it's pipelines, products or models, lean to Databricks.
- Who will run it? A BI-led team will get further on Fabric. An engineering-led team will feel constrained by it.
- How predictable is your workload? Steady use suits reserved capacity. Spiky, heavy jobs suit usage billing and isolated compute.
- One cloud or several? Anything beyond Azure points to Databricks.
Fabric is also the younger product, generally available since late 2023, and it's still gaining features quickly. Check that the specific capabilities you need are released and not only announced.
We're a Databricks Select Partner and we build on Fabric and Snowflake too. Our data platform work starts from your workloads and your team, and we'll tell you if the simpler option fits. If Snowflake is on your list as well, see Databricks vs Snowflake.
