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DataDios Lineage

Overview​

Lineage is the live map of how data flows through your stack. DataDios discovers it automatically from the warehouse, the orchestrator, and your code, then keeps it current as those sources change — so you can trace any column back to its origin and forward to every consumer without maintaining a manual catalog.

Key Capabilities​

The graph is complete and live​

Every dataset, every job, and every column-level dependency lives in one graph. It updates as your warehouse, dbt manifest, and orchestrator change — there is no separate catalog to keep current. Stale lineage is worse than none, so the graph is refreshed continuously rather than snapshotted once.

  • Granularity: column-level
  • Refresh: continuous
  • Catalog: none to maintain

Workflows carve the graph into ownership​

A workflow is a named slice of the graph that a team owns — its sources, its outputs, and the intermediate nodes between them. Two teams can have overlapping workflows; ownership here means responsibility, not exclusivity, so a shared table can belong to more than one team's view.

  • Sources: per workflow
  • Outputs: owned
  • Overlap: allowed

Propose connections discovery can't see​

Some dependencies automated discovery can't observe — a Lambda that reads from S3, a manual CSV upload. In free-form mode you can drag a connector between two nodes, and the edit goes into an approval queue (author is not the approver) rather than silently rewriting the global graph. The map stays trustworthy because every manual edge is reviewed.

Impact: what breaks if this changes?​

Select any node and the Impact view lists every downstream consumer. Before you ship a schema change, Impact tells you exactly who depends on the column you're about to drop or rename — so a breaking change becomes a conversation with the right teams instead of an outage.

Pairs with drift and quality​

Lineage is most useful next to the rest of the platform. Cross-reference it with the Metadata Timeline to see when a column changed, and with data quality checks to see whether a failing rule's root cause is an upstream table that shifted.