Snowflake Interoperable Lakehouse Lessons

This article examines the practical challenges of achieving interoperable lakehouse architectures with Snowflake, focusing on production contracts rather than marketing narratives. It covers why interoperability is harder than it sounds, especially with agentic analytics changing failure modes. The author analyzes Apache Iceberg specifications, catalog behavior, and engine contracts to distinguish between read and write interoperability. Key topics include storage layout, catalog behavior, semantic definitions, identity, quality, cost controls, and operational guardrails for multi-engine access. The article provides architectural patterns, common failure modes, and metrics for successful rollout, emphasizing that platform teams need validation, rollback, and ownership before enabling multiple writers. It offers practical advice for engineers, data owners, and executives on verifying production readiness.