Snowflake is a pure cloud data warehouse optimized for SQL analytics with consumption-based pricing, while Databricks is a unified lakehouse platform combining data warehousing and AI/ML workloads with Apache Spark at its core. Databricks excels for organizations needing integrated machine learning, while Snowflake dominates traditional analytics and BI use cases.
Snowflake vs Databricks — Side-by-side comparison on A Versus B
Choose Snowflake if your primary focus is SQL-based analytics, business intelligence, and cost-predictability with straightforward data warehousing needs. Choose Databricks if you need a unified platform for analytics AND machine learning, work with diverse data formats, or require Apache Spark's distributed processing capabilities for complex transformations.
Enterprise cloud data warehouse with native multi-cloud support and broad BI ecosystem integration.
AI and data lakehouse platform built on Apache Spark for unified analytics and ML workflows
Snowflake vs Databricks — Comparison Data
Structured comparison data for Snowflake vs Databricks with 216 attributes.
Starting Monthly Cost (10GB active data)
SQL Query Performance (TPC-DS Benchmark)
Maximum Concurrent Queries Per Warehouse
Annual TCO (100TB storage, average usage)
Query Performance (10TB TPC-DS benchmark)
SQL Query Performance (sample 1TB table)
BigQuery/Equivalent Query Speed (1TB dataset)
Data Governance (Unity Catalog equivalent)
Minimum Learning Curve (months for competency)
Starting Monthly Cost (1 TB storage + compute)
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)
Required Team Skills (FTE equivalents for operations)
Query Latency (median, standard ETL workload)
Built-in Collaboration Tools (notebooks, dashboards, repos)
Annual Cost (100TB/year, 5-node baseline)
Collaborative Notebooks with Version Control
Snowflake vs Databricks — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Snowflake vs Databricks.
Which platform is cheaper for SQL analytics workloads?
Snowflake is typically cheaper for pure SQL analytics and BI use cases, with starting costs around $120-240/month. Databricks starts at $500+/month but becomes cost-competitive when combining analytics with ML/AI workloads, as you avoid paying for multiple separate platforms.
Can I use Snowflake for machine learning?
Snowflake has limited native ML capabilities. You can use Snowflake ML (Python UDFs, basic model training) for simple tasks, but for production ML pipelines, you'll typically integrate external tools like SageMaker, Vertex AI, or Databricks ML, adding complexity and cost.
What is the lakehouse architecture and why does Databricks use it?
A lakehouse combines data lake flexibility (any data format, structure) with warehouse performance (SQL querying, ACID transactions). Databricks uses Delta Lake to achieve this, allowing raw data ingestion without ETL overhead while maintaining data quality—ideal for organizations handling diverse data types.
Which platform has better SQL query performance?
Snowflake has optimized SQL query performance (typically 2-5 seconds for standard queries on 1TB datasets) due to its columnar architecture and query optimizer. Databricks SQL is competitive but slightly slower (8-15 seconds typical) because it uses distributed Spark processing, which has more overhead for pure SQL workloads.
Is Databricks harder to learn than Snowflake?
Yes, Databricks has a steeper learning curve. Snowflake is designed for traditional SQL users and requires minimal distributed computing knowledge. Databricks requires understanding of Apache Spark, distributed processing, and cluster management, making it better for data engineers and ML practitioners but more complex for BI analysts.