Data lineage tools like Amundsen and Data Hub are essential for visualizing data transformations within pipelines, offering table-level insights for analysts and scientists. However, when deeper insights are needed, such as the origin of data in BI reports or the impact of altering a column, column-level lineage becomes crucial. Column-level lineage provides a detailed view of data flow, helping track changes or usage of sensitive data without extensive SQL code reviews. Datafold offers an intuitive interface for this purpose and integrates with existing tools in the modern data stack through its GraphQL API, facilitating the incorporation of lineage data into other catalogs. The blog highlights a practical example of integrating column-level lineage data into Amundsen using a beer-themed data pipeline, modeled in dbt and leveraging BigQuery, allowing users to gain insights into brewery locations and beer styles efficiently. While dbt natively supports table-level lineage, this example demonstrates how to scale lineage tracking using Datafold's API and Amundsen's Databuilders, ensuring seamless metadata integration and enhanced data visibility.