Tracking owners, history, dependencies, and lineage in Catalog Explorer
Unity Catalog automatically captures data lineage across tables, views, columns, dashboards, notebooks, jobs, and ML models whenever queries run through a Unity Catalog-enabled cluster or SQL warehouse. Catalog Explorer surfaces this lineage graphically, along with owner information, version history (via Delta history), and dependency relationships, without requiring any manual configuration. This objective focuses on using Catalog Explorer's UI panes to inspect and interpret these governance artifacts.
1 · Learn the must-know
- Lineage is captured automatically at the table and column level for operations run on Unity Catalog-enabled compute (shared or single-user clusters, or SQL warehouses); no separate setup or job is needed to enable it.
- In Catalog Explorer, the Lineage tab on a table or view shows upstream and downstream objects (tables, views, notebooks, workflows, dashboards) and can be explored interactively by clicking nodes to expand the graph.
- The Owner field on a catalog, schema, or table in Catalog Explorer determines who has full management rights by default and can be reassigned by a metastore admin or current owner via the Permissions/Details tab.
- The History tab in Catalog Explorer shows Delta Lake table version history (operations, timestamps, users) and is distinct from lineage, which tracks relationships between objects rather than row-level changes.
- Lineage data is retained and displayed only for interactions that occurred after Unity Catalog lineage capture was enabled on the workspace, so objects or queries run before enablement or on non-UC compute will not appear.
- Access to view lineage information is governed by Unity Catalog permissions: users must have at least SELECT/USE privileges on the objects involved to see lineage details for them, preventing lineage from leaking data about objects they cannot access.
2 · Check your understanding
A data engineer at an Azure Databricks workspace plans to deprecate a table named sales.orders_raw. Before dropping the table, the engineer wants to identify every downstream notebook, job, and dashboard that reads from it so the impact can be assessed using Catalog Explorer. Which feature should the engineer use?
What you have tried across DP-750's objectives, not a readiness score.
Set up and configure an Azure Databricks environment15-20% of the exam0 of 13 tried
Secure and govern Unity Catalog objects15-20% of the exam0 of 12 tried
Prepare and process data30-35% of the exam0 of 28 tried
Deploy and maintain data pipelines and workloads30-35% of the exam0 of 24 tried
3 · Keep going
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