Databricks vs Snowflake
Databricks and Snowflake are both evaluated in the CIOPages Cloud Data Warehouse Buyer's Guide and the Data Lakehouse Platforms Buyer's Guide. The guide groups Databricks under Lakehouse/ML and Snowflake under Multi-Cloud CDW.
In the Cloud Data Warehouse guide
| Attribute | Databricks | Snowflake |
|---|---|---|
| Approach | Lakehouse/ML | Multi-Cloud CDW |
| Best for | Data science-centric organizations and those needing unified analytics + ML on a single platform | Multi-cloud enterprises prioritizing SQL analytics, data sharing, and zero-ops experience |
From the Cloud Data Warehouse Buyer's Guide, updated March 2026. 5 vendors evaluated.
Databricks
Choose it when ML is the point, not an afterthought: industry-leading ML/AI, excellent Spark integration, Delta Lake bringing ACID transactions to data lakes, strong real-time streaming, and Unity Catalog for governance. SQL warehouse performance has improved dramatically but still trails Snowflake on pure BI, SQL-only teams face more complexity than they need, and DBU pricing rewards careful optimization.
Snowflake
The zero-ops SQL warehouse to beat: best-in-class multi-cloud portability, near-zero administration, strong SQL performance, and an excellent data-sharing marketplace, with Snowpark covering Python and ML work. Consumption pricing produces cost surprises, separating storage from compute demands careful workload management, and ML capabilities are improving but not yet at the Databricks level.
Also evaluated in this guide
Amazon Redshift, Google BigQuery, Microsoft Fabric / Synapse
In the Data Lakehouse Platforms guide
| Attribute | Databricks | Snowflake |
|---|---|---|
| Approach | Spark + AI | Managed SQL |
| Best for | Data-intensive organizations unifying data engineering, ML, and AI — and teams that want Spark depth without giving up open formats | Analytics-led organizations that prize operational simplicity and governed data sharing, and want Iceberg openness without running infrastructure |
From the Data Lakehouse Platforms Buyer's Guide, updated June 2026. 9 vendors evaluated.
Databricks
Pioneered the lakehouse and remains the strongest unified platform for data engineering, ML, and AI on one runtime: Delta Lake with the Photon engine for fast SQL, and Unity Catalog — now open-sourced — governing tables, models, and files across formats. Acquiring Tabular added full managed Iceberg tables exposed through the Iceberg REST API, and UniForm lets Iceberg and Hudi clients read Delta tables. Premium pricing plus DBU and cloud-compute costs escalate quickly without disciplined cluster governance, Spark fluency still helps for advanced work, and the deepest value sits inside the Databricks runtime even though the formats and catalog are open. Pure-SQL BI shops will find it heavier than a managed warehouse.
Snowflake
Operational simplicity is what you are paying for, and it delivers: the easiest-to-operate cloud data platform, clean separation of storage and compute, strong governed data sharing and Marketplace, and excellent SQL performance for analytics. Full Apache Iceberg support, v3 included, now extends that performance, governance, and sharing to open tables, and Snowflake created Polaris — donated to Apache, offered commercially as Open Catalog — as a vendor-neutral Iceberg REST catalog. Credit-based consumption makes budgets unpredictable without warehouse-sizing and auto-suspend discipline, Snowpark and Cortex are maturing but trail Databricks on heavy Spark and custom ML, and Horizon governance is strongest on Snowflake-managed data.
Also evaluated in this guide
AWS (Athena / EMR + S3 Tables), Cloudera, Dremio, Google BigQuery / BigLake, Microsoft Fabric, Onehouse, Starburst
Related comparisons
- Microsoft Fabric vs Databricks
- Microsoft Fabric vs Snowflake
- Cloudera vs Databricks
- Amazon Redshift vs Snowflake
Editors choose the vendors and write the evaluations. Paying CIOPages does not change how a vendor is covered. Editorial policy