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Audit and Metrics

Audit Trail and Metrics help teams understand who changed data workflows, what happened during connector and fusion runs, and whether governance queues need attention.

Use these pages when a dataset, connector, method, fusion task, review item, or rejected row affects a production workflow.

Audit Trail​

Open:

Data Integration > Audit Trail

Audit Trail can be filtered by:

  • entity type;
  • entity ID;
  • action;
  • page size.

Common entity types include:

  • dataset;
  • method;
  • fusion task;
  • pipeline;
  • review item.

Use Audit Trail​

Use Audit Trail to answer:

  • who validated a dataset;
  • when a method was published;
  • when a dataset was deprecated;
  • who approved or rejected a review item;
  • whether a fusion task was changed before a run;
  • what context was recorded for a change.

Audit review workflow​

  1. Open Audit Trail.
  2. Filter by entity type.
  3. Add action filter when needed.
  4. Open the audit entry.
  5. Review time, action, entity, target, actor, and context.
  6. Capture the audit entry in the handover or incident record if the change affected downstream workflows.

Metrics Dashboard​

Open:

Data Integration > Metrics Dashboard

Metrics Dashboard summarizes DFS Lite ingest and DFS Pro governance health.

Metrics to review​

AreaWhat to check
Connector ingestTotal connectors, active connectors, failed connectors, recent runs.
Points writtenPoints written in the recent window and failed points.
Data qualityAverage quality and connector snapshots.
Fusion tasksActive tasks, tasks in review, recent run status.
Rejection queueOpen, pending, acknowledged, fixed-in-source, and reprocessed rows.
Schema driftDrift detections and recent schema snapshots.

Connector ingest metrics​

Use connector metrics to decide whether data is arriving at the expected rate.

Check:

  • active versus failed connector count;
  • points written;
  • failed points;
  • status counts;
  • recent runs.

If connector failures grow, inspect Sync History and Connector Detail before using downstream data.

Fusion and review metrics​

Use fusion metrics to understand data processing health.

Check:

  • active fusion tasks;
  • tasks in review;
  • completed, review, and failed runs;
  • recent fusion runs;
  • open review items.

If many tasks are in review, prioritize review queue cleanup before publishing outputs.

Rejection queue metrics​

Use rejection metrics to understand ingest problems.

Check:

  • pending rows;
  • acknowledged rows;
  • fixed-in-source rows;
  • reprocessed rows;
  • replay evidence.

A growing pending queue usually means upstream data, mapping, or validation rules need attention.

Schema drift metrics​

Schema drift means observed source or dataset schema changed.

Review drift when:

  • a connector starts failing after a source change;
  • a dataset version changes;
  • downstream dashboards or fusion tasks stop working;
  • new columns appear or required columns disappear.

Operating routine​

For production workflows, review:

  1. Metrics Dashboard at the start of an operations review.
  2. Data Quality for connector-level detail.
  3. Sync History for recent ingest evidence.
  4. Review Queue for unresolved items.
  5. Audit Trail before approving handover or incident closure.
PageUse
Sync HistoryInvestigate connector run details.
Data QualityInspect connector completeness, timeliness, and accuracy.
Review QueueResolve review items and rejected rows.