Snowflake vs. Fabric: 2025 Comparison
The Cloud Data War continues. A balanced, technical look at how Snowflake and Microsoft Fabric compare in the 2025 landscape.
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Since Microsoft Fabric burst onto the scene in mid-2023, the most common question I get is: “Should we use Snowflake or Fabric?”
Two years later, in 2025, the dust has settled. Both platforms have matured. Here is the honest, engineering-focused comparison.
1. Architecture: OneLake vs. Hybrid Storage#
- Microsoft Fabric is all-in on OneLake (ADLS Gen2 underneath) and the Delta Lake open format (Parquet). If you are a heavy Microsoft shop (Power BI, Azure), OneLake is incredibly convenient. The data doesn’t move; different engines (SQL, Spark, KQL) just mount it.
- Snowflake has moved to a Hybrid model. While native Snowflake storage (micro-partitions) is still the performance king, Snowflake’s support for Iceberg Tables (via Polaris Catalog) effectively gives you a “OneLake” experience. You can store data in your own S3/Azure Blob buckets in open formats and query it with near-native performance.
Verdict: Tie. Both now support the “Separation of Storage and Compute” with open formats.
2. Compute Engines#
- Snowflake: Best-in-class SQL engine. It just works. Concurrency scaling, micro-partition pruning, and result caching are superior for high-concurrency BI serving.
- Fabric: Offers multiple engines (Synapse Data Warehouse for SQL, Spark for Engineering). The Spark integration in Fabric is excellent—spin-up times are fast. However, the SQL Warehouse endpoint can sometimes struggle with the massive concurrency that Snowflake handles effortlessy.
Verdict: Snowflake for SQL/BI. Fabric for Spark-heavy workloads.
3. The “Cortex” vs. “Copilot” AI Battle#
- Snowflake Cortex: Focused on exposing serverless LLM functions inside the pipeline (SQL/Python). It’s very developer-centric.
- Fabric Copilot: Heavily integrated into the low-code UI. Great for business users generating reports or flows, but sometimes feels like a “black box” for engineers.
4. Ecosystem Integration#
- Fabric: If you use Power BI, the “Direct Lake” mode is a game changer. No import mode, no DirectQuery latency. It reads Delta (Parquet) files directly from OneLake into Power BI memory. It’s blazing fast.
- Snowflake: Universal connectivity. Works with Tableau, Looker, dbt, Fivetran, and yes, Power BI (though not via Direct Lake… yet).
5. Cost Model#
- Fabric: Capacity-based (F-SKUs). You buy a capacity (e.g., F64) and all your workloads share it. Great for predictable budgeting, bad if a rogue Spark job eats all the CPU for the CEO’s report.
- Snowflake: Usage-based (Credits). You pay for what you use. Great for almost-infinite scaling, but requires strict governance to avoid bill shock.
Conclusion: Which one?#
It’s rarely a migration conversation anymore; it’s a coexistence conversation.
- Choose Snowflake if you need the highest performance SQL warehouse, cross-cloud capability (AWS/Azure/GCP), and powerful data sharing.
- Choose Fabric if you are deeply embedded in the Microsoft ecosystem, want a unified SaaS experience, and prioritize Power BI performance above all else.
In 2025, many of my clients use both: Snowflake as the Enterprise Data Warehouse and Cortex engine, utilizing Iceberg tables that Fabric can also read for Power BI reporting.