Google BigLake

BigLake provides enterprise storage, governance and performance for building analytical, operational and real-time AI workloads on an open lakehouse. Apache Iceberg tables managed through its Iceberg REST catalog are readable and writable from BigQuery, Google Cloud Managed Service for Apache Spark and Iceberg-compatible engines such as Spark, Trino and Flink, and Google has extended this to a borderless lakehouse reaching AWS, Databricks and Snowflake data.

Adopting Iceberg rather than a proprietary format is the decision that makes this interesting. A managed lakehouse holding data in an open table format means the data remains readable by engines the vendor does not control, which is the opposite of how warehouse lock-in has historically worked.

The cross-cloud reach is the more aggressive move. Making another platform's data queryable from here targets exactly the organisations that already committed elsewhere, and it is a bet that governance and AI access matter more to them than where the bytes sit.

Governance is what makes any of that safe to enable. Data readable from several engines across several clouds needs one place where access is decided, and a shared catalog with enforced permissions is the difference between interoperability and an uncontrolled copy sprawl.