Google Cloud's borderless Lakehouse lets BigQuery query Databricks-managed Iceberg tables

[Google Cloud] Google Cloud said its borderless Lakehouse now lets BigQuery query Databricks-managed Apache Iceberg tables directly, without copying data. According to Google Cloud, BigQuery reads the same files Databricks writes, and new changes appear after Lakehouse's next scheduled metadata refresh, which can run as often as every five minutes. The approach extends federated queries to external clouds such as Amazon S3 and Azure Blob Storage through the open Apache Iceberg REST Catalog and cross-cloud connections; remote-cloud egress charges still apply, but Cross-Cloud Interconnect (AWS) and Lakehouse caching can reduce them, Google Cloud said. On governance, administrators can configure a BigQuery Cloud Resource Connection tied to a service account that manages IAM rights for GCS buckets, enabling row-level and column-level security and dynamic data masking on external Iceberg tables. Google Cloud also said Databricks-managed Iceberg tables do not support partition transforms and that Databricks recommends liquid clustering instead.