Snowflake vs. Databricks
Cloud Data Warehouse vs. Lakehouse Platform
Snowflake dominates cloud data warehousing. Databricks pioneered the lakehouse. Your data strategy determines which platform wins.
📊 Scoring Matrix
Cloud data warehouse (SQL-first)
Lakehouse (data + AI unified)
Excellent (purpose-built)
Very good (Photon engine)
Snowpark ML (growing)
MLflow, Mosaic AI (leader)
Best-in-class marketplace
Delta Sharing (open standard)
Compute + storage (can spike)
DBU-based (can spike)
Proprietary platform
Apache Spark, Delta Lake, MLflow
📋 Executive Summary
Snowflake for SQL-centric analytics and data sharing. Databricks for ML/AI workloads and lakehouse architecture.
Both can cost 50K-500K+/yr for large deployments. Databricks open-source stack can reduce vendor lock-in costs by 30-40% long-term.
🎯 Decision Framework
- ✓ SQL-heavy analytics workloads
- ✓ Data sharing and marketplace
- ✓ BI tool integration
- ✓ Multi-cloud data warehouse
- ✓ ML/AI model training
- ✓ Lakehouse architecture
- ✓ Open-source data stack
- ✓ Unified data + AI platform
SQL analytics team? Snowflake. ML/AI team? Databricks. Both? Evaluate lakehouse vs. warehouse architecture fit for your use cases.
🌐 Market Context
Snowflake (15B+ revenue run rate) and Databricks (1.6B ARR, 2024) are the two largest modern data platforms. Both expanding into each other's territory.
Databricks growing faster (60%+ YoY) driven by AI/ML demand. Snowflake adding ML capabilities via Snowpark and Cortex.
🛠️ Related Tools
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