Snowflake and Google Cloud unite data engines via open Apache Iceberg

The core tension Snowflake and Google Cloud address is that modern data teams no longer use a single analytics engine—they combine Spark, BigQuery, Gemini Enterprise Agent Platform, Snowflake's own CoCo and CoWork, and other specialized tools within the same pipeline depending on the job. Historically, this forced a brutal choice: replicate data across systems (creating redundancy and inconsistency) or force everyone through one engine (sacrificing specialization). The industry's solution emerged from the Data Locality principle: it is faster, cheaper and more secure to move executable code to where data lives than to move massive volumes of data across a network.

Apache Iceberg provides the open table format that makes this possible. By standardizing how data is physically laid out, Iceberg lets all engines read and write directly to the same tables without proprietary adapters. But an open format alone is insufficient—multiple engines writing concurrently risk corruption and inconsistency. That is where the catalog enters: it serves as the single governance authority, managing table metadata, enforcing access policies, coordinating writers and ensuring consistency. Snowflake's Horizon Catalog and Google Cloud's Lakehouse runtime catalog both implement the Iceberg REST Catalog (IRC) standard, a universally understood API that allows any engine to discover tables and commit updates. Crucially, both catalogs issue time-limited, scoped credentials rather than standing bucket access, so engines never gain persistent access to underlying storage—a design that makes federation (bidirectional cross-catalog access) secure and practical.

For AI workflows, data accessibility alone falls short. The article identifies three remaining gaps: programmatic access (so agents, not just human analysts, can query lakehouse data), semantic grounding (so AI systems understand what data represents and do not hallucinate), and contextual intelligence (surrounding knowledge—freshness, authority, domain relationships—that helps agents interpret data correctly). Snowflake addresses these through model context protocol (MCP) servers, Semantic View Autopilot (which auto-discovers semantic models from query patterns), and Horizon Context (which governs metadata). Google Cloud offers comparable mechanisms through BigQuery's MCP server, its Universal Semantic Layer, and Knowledge Catalog. Together, these layers allow agents to inherit consistent business logic and data context across the entire lakehouse, reducing drift and hallucination.