Snowflake vs Databricks: Which Data Platform Wins?
Snowflake
Cloud data platform with separation of storage and compute. Data warehouse, data lake, and secure data sharing across AWS, Azure, and GCP.
Databricks
Unified data + AI platform built on Apache Spark. Lakehouse architecture combining data warehouse and data lake with collaborative ML.
Feature Comparison
| Feature | Snowflake | Databricks |
|---|---|---|
| SQL analytics | ✓ | ✓ |
| Multi-cloud (AWS/Azure/GCP) | ✓ | ✓ |
| Data sharing / marketplace | ✓ | ✓ |
| Zero-copy cloning | ✓ | ✗ |
| Time travel queries | ✓ | ✗ |
| Snowpark (Python/Scala/Java) | ✓ | ✗ |
| Apache Spark native | ✗ | ✓ |
| MLflow for model lifecycle | ✗ | ✓ |
| Collaborative notebooks | ✗ | ✓ |
| Delta Lake (ACID transactions on data lake) | ✗ | ✓ |
| Snowpipe (continuous ingestion) | ✓ | ✗ |
Pricing & Model
| Snowflake | Databricks | |
|---|---|---|
| Starting Price | Pay per credit | Pay per DBU |
| Free Tier | 30-day free trial | Free tier available |
| Monetization | Usage-based | Usage-based |
| Category | Data & BI | Data & BI |
| Target Audience | Data engineering and analytics teams needing elastic, multi-cloud data warehouse | Data engineers, data scientists, and ML teams building on big data infrastructure |
Which Should You Choose?
Snowflake is best for SQL-first analytics teams and BI workloads, with its standout strength being elastic multi-cloud data warehousing with zero-copy cloning, time travel, and a rich data marketplace. If your team primarily writes SQL and needs governed, instantly-queryable data for dashboards and reports, Snowflake delivers.
Databricks excels for data engineering and ML teams that need a unified platform for ETL pipelines, Spark-based data processing, and model training. With collaborative notebooks, MLflow for model lifecycle management, and Delta Lake for ACID transactions on data lakes, Databricks is the go-to for ML/AI-heavy data teams.
The right choice depends on your team's skillset: Snowflake for SQL/BI-centric teams needing a clean, governed warehouse; Databricks for engineering/ML teams building on Spark and training models at scale. Many enterprises use both — Databricks for the data engineering + ML pipeline, Snowflake for the governed analytics layer.
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