Column-Level Lineage

Column-Level Lineage provides visibility into the upstream and downstream relationships of a column.

Common use-cases for column-level lineage are

1. Source Exploration: During development, column-level lineage helps you understand how a column is derived.
2. Impact Analysis: When modifying the logic of a column, column-level lineage enables you to assess the potential impact across the entire DAG.
3. Root Cause Analysis: Column-level lineage helps identify the possible source of errors by tracing data lineage at the column level.

Usage

1. Select a node in the lineage DAG, then click the eye icon next to the column you want to view.

   

2. The column-level lineage for the selected column will be displayed.

   

Transformation Types

The transformation type is also displayed for each column, which will help you understand how the column was generated or modified.

Type	Description
Pass-through	The column is directly selected from the upstream table.
Renamed	The column is selected from the upstream table but with a different name.
Derived	The column is created through transformations applied to upstream columns, such as calculations, conditions, functions, or aggregations.
Source	The column is not derived from any upstream data. It may originate from a seed/source node, literal value, or data generation function.
Unknown	We have no information about the transformation type. This could be due to a parse error, or other unknown reason.

Limitation

Column-level lineage only displays column selection operations. It does not indicate if a column has been used in filters (WHERE clauses), with grouping (GROUP BY), joins, or other transformations.