Snowflake vs Databricks

Data platforms.

Overview

Snowflake and Databricks are the two dominant data platforms with different philosophies. Snowflake is SQL-first warehouse (separation of compute and storage, virtual warehouses, polished SQL UX); Databricks is the data lakehouse (Apache Spark, Delta Lake, MLflow, Unity Catalog, ML-first). The right answer depends on whether SQL analytics or ML/Spark workloads dominate.

• Snowflake: SQL-first warehouse. Separation of compute and storage, virtual warehouses, polished SQL UX. Default for analytics-heavy teams.
• Databricks: data lakehouse. Spark, Delta Lake, MLflow, Unity Catalog. Default for ML-heavy and Spark-native workloads.
• Operational fit per team. SQL-first analytics teams bias toward Snowflake; ML and data-engineering teams bias toward Databricks.
• Per-team choice. Different teams may pick differently in larger orgs. Document the rationale per team.

The approach

Workload-driven choice, per-team operational fit considered, documented rationale per team. The discipline is making the data platform choice once with a written reason rather than running both Snowflake and Databricks for the same workloads.

• Workload-driven. Platform per team. Reality drives the answer.
• Snowflake for SQL-first analytics. Polished SQL UX, BI-tool integration, virtual warehouses. Default for analytics teams.
• Databricks for ML and Spark. Spark-native, MLflow integration, Delta Lake. Default for ML and data-engineering teams.
• Operational fit plus documented rationale. Team workflow considered; per-team rationale captured. Future migrations have a paper trail.

Why this compounds

The right data platform compounds across years. Analytics patterns and team expertise align with the platform; cross-team tooling (BI integration, lineage, governance) gets built once and reused. By year two the platform choice is automatic per team.

• Better operational fit. Platform matches team. Velocity stays high.
• Workload-driven decisions. Replaces tribal preference with documented rationale. Quality of choice improves.
• Better engineering velocity. Right platform means analytics and ML pipelines compose cleanly. Iteration speed increases.
• Year-one investment, year-two habit. First platform choice is the investment; subsequent teams inherit the patterns.