Comparison
Column-level lineagevsImpact analysis
Column-level lineage
you need to know whether dropping this one column breaks anything, not whether the table is used.
Lineage tracked per column rather than per table, so a change can be scoped to the fields that actually feed something. Table-level lineage answers 'is this used', which for a wide table is always yes and therefore useless for making a decision. The column-level version is what turns a deprecation from a negotiation with everyone into a message to three people.
Full entry →Impact analysis
before changing the model you list every dashboard, export and reverse-ETL sync that reads it, and it is longer than you expected.
Working out what will be affected by a change or a failure, by walking lineage downstream. It is what makes a schema change a planned event rather than a surprise, and it is the first thing done in a data incident because the population affected determines who to tell. Its quality is bounded by how far lineage extends: it usually stops at the warehouse boundary, and the spreadsheet somebody built on an export is invisible.
Full entry →