Snowflake vs Databricks: How to Compare Them

Snowflake and Databricks started from different places (a cloud SQL warehouse and a Spark-based data and ML platform) and have been adding each other’s strengths for years. Comparing them by slogans is unhelpful. Compare them against your workloads.

Where each started

	Snowflake	Databricks
Origin	Managed cloud data warehouse	Managed Spark platform, then lakehouse
Primary interface	SQL	Notebooks, Python/Scala/SQL, SQL warehouses
Default storage model	Managed storage in Snowflake’s format, plus support for open table formats	Delta Lake tables on your object storage, plus support for other open formats
Governance	Built-in role-based access, policies	Unity Catalog

Questions that decide

1. What are the main workloads? Mostly SQL analytics and BI favours a SQL-first platform. Heavy data engineering in Python, streaming and machine learning favour a Spark-first platform. Mixed workloads need you to test both.
2. Where should the data live? If owning data in open formats on your own storage is a requirement, check each platform’s current support for open table formats and external engines.
3. Who will use it? Analyst-heavy teams value SQL simplicity; engineering-heavy teams value code, notebooks and library ecosystems.
4. How much operation do you want? Both are managed, but you still size compute, manage costs and govern access differently.
5. What does it cost for your workload? Pricing models differ (credits and compute types). Run a representative proof of concept and measure; published benchmarks rarely match your queries.

How to run a fair evaluation

• Choose three to five real workloads: a large transformation, a dashboard query set, a streaming or incremental load, and an ML feature job if relevant.
• Implement each idiomatically on both platforms rather than porting one platform’s design to the other.
• Measure runtime, cost, developer effort and operational steps.
• Include governance: access control, masking, lineage and auditing for your compliance needs.

Common mistakes

1. Choosing from vendor benchmarks instead of your own workloads.
2. Ignoring team skills and hiring.
3. Underestimating governance and cost-management effort.

Interview relevance

If asked to compare, give a framework (workloads, data ownership, users, operations, cost) and a recommendation for the stated scenario. Avoid absolute claims.

Key takeaway

Evaluate on your workloads: SQL-first analytics, Python-heavy engineering and ML, data ownership and cost. Then test both.