Snowflake vs Databricks: Which Cloud Data Platform is Right for You?
Snowflake and Databricks are the two dominant cloud data platforms, but they solve different problems. As a Snowflake-focused consultancy, we’re upfront about our expertise — but we believe in recommending the right tool for the job. Here’s an honest comparison.
Architecture: Different Foundations
Snowflake is a cloud-native data warehouse built for structured and semi-structured data. It uses a unique multi-cluster shared data architecture that separates storage from compute, allowing independent scaling.
Databricks is built on Apache Spark and designed as a unified analytics platform. It started as a big data processing engine and has evolved into a lakehouse architecture that combines data lake flexibility with data warehouse structure.
When to Choose Snowflake
Snowflake excels when your primary needs are:
- SQL-first analytics: Your team works primarily with SQL for data transformation and analysis
- Data warehousing: You need reliable, performant structured data storage and querying
- Simplicity: You want a managed service that requires minimal infrastructure expertise
- Concurrency: Many users need to query data simultaneously without performance degradation
- Data sharing: You need to share data across organizations or business units securely
When to Consider Databricks
Databricks may be a better fit when:
- Machine learning is central: Your primary use case involves training and deploying ML models at scale
- Unstructured data processing: You work extensively with streaming data, images, or other unstructured formats
- Python-first team: Your data team works primarily in Python/PySpark rather than SQL
- Real-time streaming: You need sub-second streaming analytics with Spark Structured Streaming
Key Differences
Pricing
Snowflake uses consumption-based pricing with credits for compute time. Costs are predictable and tied to warehouse usage. Databricks prices by DBU (Databricks Unit) consumption, which varies by workload type and tier.
Ease of Use
Snowflake is generally easier to get started with, especially for teams with SQL skills. Databricks has a steeper learning curve but offers more flexibility for advanced workloads.
Ecosystem
Snowflake integrates tightly with the modern data stack (dbt, Fivetran, Matillion, Omni). Databricks has its own ecosystem including Delta Lake, MLflow, and Unity Catalog.
Data Governance
Both platforms offer robust governance features. Snowflake has native governance with access controls, masking, and tagging. Databricks offers Unity Catalog for unified governance across the lakehouse.
The TCO Reality
Vendor pricing comparisons rarely tell the full story. Total Cost of Ownership (TCO) must include the people, training, and operational overhead required to run the platform.
We’ve seen this firsthand: a prospect spent three years and $1.5 million on Databricks and had only managed to load their raw (bronze) data. No transformations, no analytics, no business value delivered. The platform’s complexity consumed all their budget and timeline.
Databricks requires significantly more engineering expertise to configure, optimize, and maintain. When you factor in the additional headcount, training, and operational overhead, we have never seen a case where Databricks delivers a lower TCO than Snowflake for analytics and data engineering workloads.
The Convergence
Both platforms are converging. Snowflake has added support for Python, Snowpark (DataFrame API), ML functions, and Iceberg tables. Databricks has improved its SQL analytics capabilities and added traditional BI-style features. The gap is narrowing.
Our Recommendation
For analytics, data engineering, BI, and most enterprise data workloads, Snowflake is the clear choice. It’s simpler to operate, faster to deliver value, and has a lower total cost of ownership when you account for the full picture.
Databricks has legitimate strengths for specific Apache Spark use cases: real-time streaming, large-scale machine learning training, and workloads that genuinely require distributed computing frameworks. If you need those capabilities, feed that specific data to Databricks for those specific workloads.
But don’t try to do everything on Databricks. Use the right tool for each job — and for most data work, that tool is Snowflake.
Some organizations use both — Snowflake as the primary data platform for warehousing, transformation, and analytics, with Databricks handling only the workloads that specifically require Apache Spark. This is a valid data architecture when the use case genuinely demands it.
Need Help Deciding?
We help companies evaluate their data platform options and plan cloud migrations based on actual requirements, not vendor marketing. Schedule a free consultation to discuss your specific needs.