# Data Lineage
source: https://docs.chalk.ai/docs/data-lineage

## Explore how features are computed and what they depend on, with views by feature, resolver, and source column.

Data lineage tracks the complete path from a raw data source to a computed Chalk feature. For every feature produced by a SQL File Resolver, Chalk records which datasource and table supplied the raw data, which resolver transformed it, and which queries consume it as input or output. The Data Lineage page in the dashboard makes that graph browsable.

Data lineage is available for features produced by SQL File Resolvers. Features computed by Python resolvers do not appear.

To open the page, select Catalog › Data lineage in the project sidebar.

### List view

The list view presents lineage as a multi-column drill-down. You start from data sources on the left and progressively narrow to a specific feature on the right. Selecting a feature opens a detail panel that shows:

- Resolved from columns — the source column names from the SQL resolver's query
- Output of queries — named queries that list this feature as an output
- Input to queries — named queries that use this feature as an input
- A link to the resolver that produces it

Use the Group by dropdown in the toolbar to choose among three views, each of which adds a different intermediate column to the drill-down.

### Feature view

Data Sources → Tables → Features

The default view. Select a datasource, then a table, then a feature. Use this when you want to answer "given this datasource and table, what Chalk features does it feed?"

### Resolver view

Data Sources → Tables → Resolvers → Features

Adds a Resolvers column between Tables and Features. Select a resolver to see only the features that particular resolver produces from the selected table. If the selected feature is also produced by other resolvers, those appear in an Other Source Resolvers section of the detail panel.

Use this when you want to understand what a specific resolver contributes, or when multiple resolvers read from the same table and you need to separate their outputs.

### Column view

Data Sources → Tables → Columns → Features

Adds a Columns column that lists the raw source column names from the SQL query. Select a column to see which Chalk features are derived from it.

Use this when you need to trace a specific raw database column forward to the Chalk features it affects — for example, to assess the impact of a schema change in a source system, or to document which features carry data from a particular field for compliance purposes.

### Graph view

The graph view renders the same lineage data as a directed acyclic graph. Nodes flow left to right: datasources, datasource tables, resolvers, features, and scheduled queries. Selecting a feature scopes the graph to the subgraph reachable from that feature, hiding unrelated nodes.

To open the graph for a feature, select it from the Select a Feature dropdown in the top-right corner of the graph tab. Each selected feature opens in its own tab; use the + button to open additional tabs and compare features side by side. Tabs are closeable and persist as long as the page is open.

Click any node in the graph to see its detail panel on the right side of the page.

If the banner reads N scheduled queries not shown, those queries have no retained query plan from their most recent run and cannot be placed in the graph. This does not affect the list view.

### Use cases

Impact analysis — before modifying a resolver or changing a source table's schema, use the Feature or Column view to see which downstream features and queries would be affected.

Debugging — trace a feature back through its resolver and source columns to identify where unexpected values may be introduced.

Compliance — document which raw data fields feed a given feature, and which queries consume it, to satisfy data provenance requirements.

Optimization — identify resolvers that read from the same tables and evaluate whether their queries can be consolidated.