Snowflake vs. Databricks: An AI and Enterprise Readiness Evaluation
Authors:
October 29, 2026
Data lakehouses have become the operational backbone of data-driven enterprises, and increasingly, of enterprise AI. That role raises the bar for what a platform must be: open and interoperable, with enterprise-grade security and efficient price-performance at its core, and agentic AI capabilities layered on top, not just scalable storage and compute.
To test that bar directly, Signal65 built the same realistic customer scenario, utilizing the US DOT Flight Delays dataset, on both Snowflake and Databricks: ingesting data, transforming it, applying governance and PII protection, building an application, failing over for disaster recovery, and sharing data externally. Tests were attempted twice on each platform: once manually, and once using each platform’s agentic AI tooling.
The same engineers, the same dataset, and the same requirements were used throughout, so any differences in time, effort, or capability largely reflect the platforms themselves rather than the person using them.
Key Highlights:
4.6x faster with agentic development
CoCo rebuilt the same Snowflake workflow nearly autonomously, in 68 minutes versus 316 manually
Cross-cloud disaster recovery
AWS to Azure failover as a configured feature, not a custom build
32% faster governance
Masking, tagging and access policies, hands-on vs. Databricks
- Snowflake CoCo completed the full seven-phase workflow in 68 minutes versus 316 minutes for the manual workflow, 4.6x faster.
- CoCo completed every phase with minimal human interaction, including steps Genie Code did not complete in this evaluation.
- Snowflake provides a first party agent that spans in-platform and local development.
- Snowflake's CoCo activates across the full data platform, operating both inside Snowflake and directly on the developer's own machine, with no third-party tooling required — only CoCo's own CLI, a one-time three-minute install.
- In this evaluation, Genie Code did not complete the full external application workflow without manual intervention. Completing a comparable agentic experience required a third-party agent (Claude Code) that took roughly 4x longer.
- Snowflake configured AWS-to-Azure failover in 30 minutes; the evaluation did not reproduce an equivalent cross-cloud capability on Databricks.
- Snowflake completed the same governance and PII-protection tasks 32% faster than Databricks' separate admin console, a gap likely to recur as roles or policies change.
Research commissioned by:
