04. Databricks vs Snowflake

Databricks and Snowflake are both major modern data platforms, but they evolved from different architectural starting points.

High-level comparison

Capability	Databricks	Snowflake
Architectural heritage	Lakehouse / Spark	Cloud data warehouse
SQL analytics	Strong	Strong
Spark	Native foundation	Not the primary engine
Data engineering	Strong	Strong
Streaming	Strong	Strong
Open lake storage	Core architectural concept	Supported through multiple features
ML / AI	Deep platform integration	Strong and expanding
Governance	Unity Catalog	Snowflake governance capabilities
Developer styles	SQL, Python, Spark, APIs	SQL, Snowpark, APIs

Selection criteria

Ask:

• Where does data currently live?
• Is Spark required?
• How much SQL-only analytics exists?
• What are BI requirements?
• What is the ML/AI strategy?
• What cloud architecture already exists?
• What skills does the organization have?
• What governance model is required?
• What is the total cost of ownership?

Hybrid architecture

                 Cloud Data Estate
                        │
              ┌─────────┴─────────┐
              ▼                   ▼
         Databricks           Snowflake
              │                   │
       Engineering / ML       SQL Analytics

The correct answer is workload-specific, not vendor-specific.

Interview answer

A senior engineer should compare architecture, workload fit, governance, integration, skills, operating model, performance requirements, and cost rather than declaring one platform universally superior.

Next

Continue with the next topic in 01. Databricks Foundations.