Data federation, exemplified by tools like GlareDB, simplifies the process of querying and joining data from multiple sources without the need for complex infrastructure or data migrations. In a scenario where New York City real estate sales data is stored in Snowflake and a lookup table for borough names is kept in PostgreSQL, GlareDB allows users to join these disparate data sets efficiently. By using specific functions such as read_postgres() and read_snowflake(), data from both databases can be queried and combined into a unified pandas DataFrame, thus bypassing traditional extract, transform, load (ETL) processes. The demonstration, which can be followed in a Jupyter Notebook or via video walkthroughs, illustrates how GlareDB facilitates data federation by treating results from different databases as though they were in the same location, enabling seamless querying and data integration.