Connect every system in your stack
Know exactly what breaks downstream before anyone touches a CRM field.
See what breaks downstream before a property rename, workflow change, or lifecycle update reaches your warehouse.
Verify that ARR, MRR, and churn numbers mean what you think they mean, from the board deck back to the Stripe event.
Connect producers, topics, and consumers so lineage does not break at the streaming boundary.
See the full picture: what feeds every table and what depends on it across the stack.
Map every dataset, table, and scheduled query to its upstream sources and downstream consumers across the full stack.
Trace dependencies across notebooks, Delta tables, Unity Catalog, and every tool outside Databricks that depends on them.
Map every table, view, and stored procedure to its full upstream and downstream footprint across the stack.
Go beyond the DAG: trace every model through the full platform, not just the project.
Bridge the gap between execution order and data dependencies so pipeline failures are scoped by business impact.
Connect Dagster assets to every source, consumer, and BI tool outside its scope so the asset graph becomes a full-stack view.
See how SQLMesh models connect to upstream sources and downstream consumers across the entire stack.
Catch semantic drift before agents start producing wrong answers by tracing every MetricFlow definition to its upstream sources and downstream consumers.
Verify that every metric means what you think it means, from definition to source.
Surface the business logic hidden inside LookML so impact analysis doesn't stop at the BI layer.
Make calculated fields, LOD expressions, and data blending logic visible to the rest of the platform for impact analysis.
See which Hex notebooks and published apps break when upstream data changes, even the ones nobody formally tracks as production.
See the full inventory of saved questions and dashboards that depend on each warehouse table, including ones the data team does not know about.
Ground Claude Code in the full platform graph so its reasoning about data changes accounts for every system and consumer downstream.
Give Codex a unified understanding of the full data platform so it can plan and validate changes that span multiple systems.
Surface cross-system dependencies directly in the editor so data platform changes are scoped before they leave the IDE.
See the full downstream blast radius before a connector change ripples through everything that depends on it.
Trace every connector through the warehouse and into the dashboards and models that depend on the data it lands.
Map the circular paths reverse ETL creates so bad transformations do not corrupt the source systems they write back to.
Annotate every pull request with its full cross-system blast radius so reviewers evaluate changes in the context of the entire platform.
Let anyone in the organization find data assets, trace lineage, and get ownership context directly in Slack.
Trace DAG failures across Astronomer deployments to the dashboards, metrics, and teams that are affected.
Trace any Databricks AI/BI answer back through definitions, the lakehouse, and source systems so users can verify the reasoning.
Surface the modeling layer inside Power BI so impact analysis and lineage do not stop at the BI boundary.
Trace any Cortex answer back through the semantic layer, warehouse, and source systems so users can verify why the agent produced a specific result.
Trace any WisdomAI answer through the Context Layer, the warehouse, and the upstream pipeline that built the data, so users can verify results end to end.