Apache Iceberg Lakehouse: Snowflake & Google Cloud

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The post, co-authored by Snowflake and Google Cloud engineers, describes the architectural rationale behind their joint Apache Iceberg lakehouse. The core problem: giving different teams their preferred analytics engines (Spark, BigQuery, Snowflake) historically required copying data across systems, introducing consistency and security risks. Their solution applies the data locality principle: instead of moving data to each engine, all engines read and write to the same data files in customer-owned storage buckets. Apache Iceberg—originally developed at Netflix for petabyte-scale table management, later donated to the Apache Software Foundation—serves as the shared open table format. The catalog layer handles governance: it knows what tables exist, enforces access policies, coordinates concurrent writers, and returns short-lived, narrowly scoped vended credentials rather than long-lived standing bucket access to each engine. The post promotes Snowflake's CoCo and CoWork features alongside BigQuery and the Gemini Enterprise Agent Platform. The technical architecture described is genuine and reflects real industry direction, but the framing is a vendor joint announcement rather than independent analysis. For enterprise data teams, the practical implication is that the Apache Iceberg plus catalog pattern is becoming the de facto standard for multi-engine data architectures, with two of the largest cloud data platforms now explicitly aligned behind this approach.