Organizations often struggle with fragmented data spread across multiple specialized platforms and clouds. Previously, this required either building complex data pipelines or accepting incomplete reporting. Traditional methods involve copying data into central warehouses, leading to synchronization issues, storage costs, and broken pipelines when schemas change. Google Cloud's borderless Lakehouse, built on Apache Iceberg, offers a solution by enabling compute and AI to access data in place across different platforms. This architecture synchronizes metadata while allowing direct access to data files. It supports federating data from Databricks, Snowflake, and AWS Glue into BigQuery without data migration. Federated queries in BigQuery use a four-part path to access data across these external catalogs. This integration allows for unified querying and analysis of disparate datasets. Furthermore, federated tables can be used with BigQuery's conversational analytics for exploration using natural language. Operational considerations include managing background refresh timing, understanding query caching, and ensuring data residency compliance. The borderless Lakehouse aims to simplify data activation by eliminating costly migrations and complex pipelines.