Datasource lineage
The Datasource lineage view describes the lineage graph for a datasource.
Use it to understand which raw inputs produced a datasource, how ingestion contributed to the datasource, and which branches or increments are represented in the lineage graph.
When to use
Generate the view
Generate the Datasource lineage view from a datasource.
The request only needs view_type set to DATASOURCE_LINEAGE.
Output
The response follows the shared Response structure format.
The Datasource lineage view returns multiple named dataframes inside view_data.
Interpret the result
The Datasource lineage view is easiest to read as a graph: start with the nodes, then use the edges to understand how those nodes are connected.
Start with datasource nodes
Use nodes to identify the datasource node and its recorded metadata.
Rows with node_type set to DATASOURCE describe datasource-level lineage information such as row counts, train/test split counts, input file counts, increment metadata, branch information, and datasource identifiers.
Identify raw inputs
Use rows where node_type is RAW to identify raw input nodes.
Raw nodes represent upstream inputs that contributed to the datasource. Their labels can be used to connect the raw source to the datasource through the edges dataframe.
Follow edges
Use edges.link_from and edges.link_to to understand the direction of lineage.
Each edge connects a source node to a target node. link_type describes the relationship, such as ingestion. source_branch and target_branch describe the branch relationship when branch information is available.
Review increments and branches
Use increment_label, increment_index, branch, and source_branch to understand how datasource versions or increments relate to each other.
This is useful when a datasource has been updated over time or when you need to audit which branch produced the current datasource state.
Check data volume
Use n_rows, n_rows_train, n_rows_test, n_rows_masked, n_input_files, increment_size_mb, increment_mask_size_mb, and total_increment_size_mb to review the size and split of each datasource increment.
These fields are useful for verifying that the datasource contains the expected amount of data before onboarding, training, or review.
Field reference
The Datasource lineage view returns dataframes, so individual columns are not documented as standalone API schema properties.
nodes
The nodes dataframe contains the graph nodes.
edges
The edges dataframe contains the graph edges.