The borderless Lakehouse enables unified access to an organization's complete data estate across multiple clouds. Previously, integrating distributed data required complex ETL pipelines and costly data transfers. The Lakehouse allows querying data in place by federating to various cloud catalogs and offers reduced transfer costs through Partner Cross-Cloud Interconnect. New enhancements further optimize cross-cloud query costs by minimizing data transfer.Cross-cloud caching for Lakehouse transparently accelerates queries and cuts remote transfer costs by locally caching frequently accessed data. This caching operates at a sub-file block granularity, encrypts data at rest, and maintains tenant and regional isolation. Freshness checks ensure data accuracy by verifying if remote data has changed before serving cached results. In practice, this caching significantly reduces the amount of data transferred, often to under 5% of the processed data.This efficiency leads to a lower Total Cost of Ownership, making cross-cloud analytics and AI viable at an enterprise scale. Combining this with Iceberg's compression can reduce network transfers to under 3% of the total data processed. BigQuery cross-cloud connections are also available in preview, allowing direct querying of non-Iceberg data in other clouds and accelerating workloads. These connections leverage BigQuery compute workers in Google Cloud regions, offering global availability and full BigQuery feature parity.