AI Data Engineering
Root Cause Analysis
for Data Pipelines
Trace and fix data incidents across your warehouse, orchestrator, code, lineage, and schemas.
Trace the failure
across the stack
Identify the Root Cause
A failed task or model is not always the cause of the incident.
Upriver correlates warehouse state, orchestration activity, schemas, transformation logic, dependencies, and recent changes to identify the condition that explains the failure.
Validate the Resolution
A successful rerun does not necessarily mean the data is correct.
Upriver can generate validation queries and checks alongside the proposed remediation to verify freshness, completeness, expected data behavior, and downstream recovery.
See this in your own stack.
Investigate a Data Incident
How Upriver Performs
Root Cause Analysis
Upriver performs root cause analysis from a continuously updated model of the data environment
rather than from an isolated alert.
RCA with the Full Context
Investigate an incident without manually reconstructing the pipeline across separate tools.
Upriver already maintains the relationships between your warehouse, orchestration, code, schemas, lineage, and dependencies. The agent uses the relevant context during the investigation and follows the incident across system boundaries.
Incident → Evidence → Root cause → Remediation
The engineer reviews the reasoning and proposed changes before anything is approved.
Proactive Investigation Across All Data Operations
Preserve the context behind recurring incidents
Upriver’s agents continuously monitor the environment for changes and conditions that may affect pipeline health.
When something breaks, you don't just get an alert - you get the root cause, the fix, and the next actions.
Upriver retains operational and team-defined context so subsequent investigations can use what has already been learned about the environment.
What powers it
Root cause analysis grounded in the actual data environment
Upriver maintains a connected understanding of the systems that determine how your pipelines execute and how data moves through them.
Cross-stack context
Connect pipelines, DAGs, tables, models, schemas, code, lineage, dependencies, and operational history.
This allows an investigation to move from a downstream symptom toward the upstream systems capable of causing it.
Evidence-backed reasoning
Upriver evaluates the signals around an incident together, not in isolation.
It connects the suspected root cause to the execution history, data state, code, schema, and dependency evidence that supports the conclusion.
Persistent operational memory
Upriver carries forward knowledge about the environment: previously established context.
That means recurring failures can be interpreted with knowledge from earlier investigations instead of forcing engineers to rediscover the same dependency behavior or system-specific edge cases.
Time-aware environment state
Upriver maintains changes in pipeline runs, schemas, queries, transformations, and configuration.
This allows an investigation to compare the last known-good state with the first bad state and isolate changes that occurred on the relevant dependency path.
Try for free
Integrate anywhere your data team works
Same agent. Same context.
Wherever you work.
Slack
Tag Upriver in any channel or thread. DM for a quick question. Kick off a task from Slack and continue in the web app.
- Slack-native replies with task links
- Reply to incident notifications directly
External Agent Plugin
Upriver’s context layer, lineage, and warehouse access with any agent you’re already using. Same stack knowledge, wherever you work.
- No context feeding needed
- Works alongside your existing workflow
Upriver UI
Every piece of data work, done without leaving Upriver. Warehouse, code, catalog, pipelines, and git ops - all in one place.
- Run multiple tasks in parallel without jumping between tools
- Full visibility into everything your team is running and approving
Bring a real task.
See it done in minutes.
Your backlog isn't shrinking. Your stack is getting more complex. Upriver handles the work that's slowing you down - so your team can actually move faster.