Snowflake vs Databricks

Overview

Snowflake and Databricks are the two dominant platforms for modern data analytics, but they come from different origins and philosophies.

Snowflake started as a cloud data warehouse and expanded into a "Data Cloud" platform. It's known for ease of use, separation of storage/compute, and zero-maintenance operations.

Databricks started as the commercial entity behind Apache Spark and evolved into a unified analytics platform. It pioneered the "lakehouse" architecture with Delta Lake, combining data lake flexibility with warehouse performance.

Feature Comparison

Pricing

Snowflake

• •Model: Credit-based consumption
• •Compute: $2-4+ per credit (varies by edition/cloud)
• •Storage: ~$23/TB/month
• •Free Trial: 30 days, $400 credits
• •Note: Easy to understand but can get expensive at scale

Databricks

• •Model: DBU (Databricks Unit) consumption
• •Compute: $0.07-0.55+ per DBU (varies by workload type)
• •Storage: Cloud provider rates (your S3/ADLS/GCS)
• •Free Trial: 14 days
• •Note: More complex pricing but potentially cheaper at scale

Best For

Choose Snowflake if:

• •You're primarily doing SQL analytics and BI
• •You want minimal operational overhead
• •Your team is SQL-first (analysts, analytics engineers)
• •You need simple, predictable pricing
• •Data sharing is important to your business
• •You want the easiest onboarding experience

Choose Databricks if:

• •You're doing significant ML/AI work
• •You need advanced data engineering (Spark)
• •Your team includes data scientists and ML engineers
• •You want open formats (avoid lock-in)
• •You have streaming use cases
• •You want a unified platform for all data workloads

Pros & Cons

Snowflake

Pros:

• •Best-in-class ease of use
• •True zero maintenance
• •Excellent SQL performance
• •Data Marketplace ecosystem
• •Simple to understand pricing
• •Great for BI workloads

Cons:

• •Premium pricing (most expensive)
• •Limited ML capabilities
• •Proprietary format (though Iceberg support added)
• •Less flexible for non-SQL workloads
• •Snowpark still maturing

Databricks

Pros:

• •Excellent for ML/AI workloads
• •Open source foundation (Spark, Delta Lake)
• •Unified platform for all data work
• •Potentially cheaper at scale
• •Great streaming support
• •Strong governance (Unity Catalog)

Cons:

• •Steeper learning curve
• •More operational complexity
• •Complex pricing model
• •BI experience less polished
• •Requires more expertise to optimize

Architecture Differences

Snowflake: Cloud Data Warehouse

• •Data stored in proprietary format
• •Separation of storage and compute
• •Query optimization handled automatically
• •Scaling is transparent

Databricks: Lakehouse

• •Data stored in open Delta Lake format on your cloud storage
• •You own the underlying data
• •More control over compute clusters
• •Can access data outside Databricks

Integration Ecosystem

Both integrate well with the modern data stack:

• •dbt: Both have excellent dbt support
• •ETL: Both work with Fivetran, Airbyte, etc.
• •BI: Both connect to Looker, Tableau, etc.
• •Orchestration: Both work with Airflow, Dagster, etc.

Verdict

For analytics-first teams: Snowflake's ease of use and SQL focus make it the better choice for teams primarily doing BI and analytics.

For ML-heavy teams: Databricks' unified platform and MLflow integration make it better for teams doing significant ML work.

For future flexibility: Databricks' open format approach reduces lock-in, while Snowflake's growing Iceberg support bridges the gap.

Many organizations use both: Snowflake for BI/analytics, Databricks for ML/data science. The "vs" is increasingly becoming "and."