Since modern data platforms converge toward the Cloud & the Lakehouse architecture, what are the differentiators? Which one is the best for you?
You are in the process of selecting a data platform, or you would like to modernize your data warehouse, data lake we have compared Microsoft Fabric, Databricks and Snowflake, here are the 4 main differences we have found:

Availability and pricing differences
Whether you are cost sensitive, or have some existing Cloud strategy, or sovereignty requirements, you must choose the cloud and price model that fits:
- Fabric is only available on Azure and is based on a capacity pricing model.
- Snowflake and Databricks are pay-as-you-go with optional reservation, they are on the 3 main hyperscalers.
- Databricks is also available on SAP Business Data Cloud.

Data usage & consumption differences
You may start with BI requirements, still you are also interested by machine learning and GenAI for new use cases. Which platform has the best potential?
- Fabric’s Power BI is the leader in BI. Snowflake and Databricks are challenging this status with lighter dashboards.
- All three platforms are investing heavily in GenAI. Databricks has recently scored a few big partnerships with Anthropic for Claude 3.7 and Meta for Llama 4

Interoperability and resilience through Open Standards support
Transitioning to or from a data platform is significantly influenced by open standards promoting interoperability. The ability to ingest and export data is crucial, meaning that open data formats are essential to support strategic orientations:
- Databricks supports many open-source technologies (Apache Spark, MLFlow, Delta Lake, Unity Catalog) but also some new proprietary ones (Delta Live Table, Lakeflow, Mosaic AI…).
- Fabric is also a promoter of Spark, MLFlow and Delta. Other features are closed source (Power BI, Onelake, Warehouse…)
- Snowflake has opened its architecture through strong support for Iceberg tables, and open-sourced Polaris catalog. The main engines and frameworks remain closed (Warehouse, Snowpark, Cortex…)

Code development and collaboration differences
This is likely the most complex — yet one of the most important — aspects to evaluate: how are developers and users interacting with the platform? Does it provide a user-friendly environment for data analysts, data engineers, data scientists, or all of them? Is it possible to collaborate within a Scrum team (7 people), or even larger teams like in SAFe setup? The answer has direct implications for staffing, onboarding, and broader organizational acculturation.
- Snowflake has a clean from the ground-up construction with everything declared as code into the catalog using SQL or Python. All code is versioned in GIT.
- Fabrics is expanding the Power BI organization of objects in Workspaces. Versioning is JSON declarations, one repository per Workspace.
- Initially, Databricks was organized around workspaces (single workspace per session) but is now moving toward a mix of deployable assets (compute, dashboards, notebooks…) and other assets managed in the Unity catalog (tables, volumes, AI models). Versioning is a mix of Python/SQL code and JSON declarations.
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Antoine Hue
Data Architect
Introducing Antoine Hue our data expert. Antoine is a data architect for the data migration and analytics platforms, and he is responsible for the data engineering team in Romandie.