Where they genuinely differ
Snowflake began as a cloud data warehouse and grew outward into data science and application workloads. Databricks began as a Spark platform and grew inward toward warehousing and SQL. Both now credibly cover the middle ground, which is why feature-by-feature comparisons age badly.
The durable differences are operational. Snowflake optimizes for a SQL-centric team that wants very little infrastructure surface. Databricks optimizes for a team comfortable with notebooks, Spark and a more configurable runtime.
Questions that actually decide it
- Who maintains this in eighteen months — analytics engineers or data engineers?
- Is the dominant workload SQL transformation, or ML feature engineering at scale?
- How much unstructured and semi-structured data is in scope?
- What is already in the estate — Fabric, Synapse, a Spark footprint?
- How is the organization's cost governance structured, and which pricing model is easier to attribute?
The pattern we see most
Organizations with a strong BI heritage and a SQL-fluent team tend to move faster on Snowflake. Organizations with an existing Spark investment or heavy ML workloads tend to move faster on Databricks. Both statements are about velocity, not ceiling — either platform will support a mature estate.
Where both are already present, resist the instinct to consolidate immediately. Establish shared governance and a single catalog first; platform consolidation without that is just a migration with extra steps.