Lakehouse Federation is the Databricks feature built for querying external systems without moving all data into Databricks . Databricks documentation describes it as the platform for query federation , enabling users to run queries against multiple external data sources while keeping governance centralized through Unity Catalog . Databricks also documents support for external systems such as Amazon Redshift and other databases through connections and foreign catalogs, allowing read-only access to external data while managing permissions in Unity Catalog. This aligns directly with the requirement to minimize duplication and still maintain centralized governance. Databricks Connect is for local development against Databricks compute, not federated querying. MLflow is for machine learning lifecycle management. Delta Lake is a storage format and table layer, not a federation framework. Therefore, when the goal is unified governance across Databricks and external systems like MySQL, Redshift, and BigQuery without first ingesting the data, Lakehouse Federation is the documented answer. Databricks does note that for high-volume production ingestion, managed connectors may sometimes be preferred, but for direct querying without data movement, Lakehouse Federation is the correct feature.