When a CIO decides to migrate their SQL Server/SSIS infrastructure to the cloud, the question invariably comes up in the first few minutes of the scoping call: Snowflake or Fabric? This article gives you the technical and strategic elements to make the right choice.

Two fundamentally different philosophies

Snowflake is a pure cloud data warehouse. It does one thing — store and compute relational data — and it does it exceptionally well. Storage and compute are fully separated, which means you only pay for compute when queries are actually running.

Microsoft Fabric is an integrated analytics platform. It brings together in a single environment the Lakehouse (OneLake), the Data Warehouse, integration pipelines (Dataflow Gen2), notebooks (Spark), Power BI, and data governance tools.

Put differently: Snowflake is a specialised tool, Fabric is a generalist platform. Neither is "better" — they answer different needs.

Criterion 1: your existing ecosystem

If your organisation is already on Azure with Power BI as the main reporting tool, Active Directory for authentication, and a culture of Microsoft tools, Fabric is the natural extension.

If your organisation is multi-cloud (AWS + Azure, or GCP + Azure), or if you have a strategic need to avoid vendor lock-in, Snowflake est le choix logique. Snowflake works identically on AWS, Azure, and GCP. You can even replicate data across clouds without any external pipeline.

Criterion 2: the cost model

This is often the decisive criterion, and also where simplistic comparisons are most misleading.

bills storage (around $23/TB/month compressed) and compute (credits consumed per second) separately. Compute can be started and stopped automatically. For sporadic workloads (nightly batch processing, ad hoc queries), you only pay when compute is running.

bills by capacity (F2, F4, F8... up to F2048). Capacity is shared across all components: pipelines, warehouse, notebooks, Power BI. The advantage is predictability: you know exactly how much you’ll spend each month.

Generally, Snowflake is more cost-effective for sporadic workloads with large volumes, and Fabric is more cost-effective medium-scale continuous workloads within a Microsoft ecosystem.

Criterion 3: your team's skills

If your data team is comfortable with pure SQL and prefers a "code-first" approach with Git, DBT, and CI/CD workflows, Snowflake + DBT is a natural fit. The ecosystem is mature, the community is active, and best practices are well documented.

If your team is more "low-code" oriented or comes from an SSIS/Data Factory background with graphical interfaces, Fabric is more accessible. Dataflow Gen2 offers a familiar drag-and-drop experience.

Criterion 4: le cas hybride

This is a scenario we are seeing more and more: Snowflake for massive storage and heavy compute (transformation processing, data science), and Power BI/Fabric for the analytics and reporting layer. The two coexist very well via Direct Lake and external tables.

This hybrid architecture combines the best of both worlds: Snowflake's elasticity and independence for storage and transformations, la puissance integration de Fabric/Power BI for consumption et le partage.

Our position: Decinova works with both platforms and has no commercial preference. The audit of your existing estate reveals the real technical constraints that guide the right choice. We commit to a platform recommendation only after data, not before.