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Observability gives you a single place to see whether data arrived on time, whether quality checks passed, and whether costs trend in the wrong direction. You move from reactive tickets to proactive fixes using dashboards, diagnostics, DLQ tooling, cost signals, and alerts.

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Capabilities

Dashboard

Health summaries, run history, and quality signals at a glance.

Data diagnostics

Profile datasets and investigate anomalies.

Dead letter queue

Manage failed records across pipelines and syncs.

Cost insights

Track spend drivers and optimization hints.

Alerts

Notify the right people when behavior drifts.

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How teams use observability together

  • Data engineers watch failure rates, duration regressions, and DLQ depth.
  • Analytics engineers monitor semantic freshness and schema monitors tied to dashboards.
  • Finance reviews cost insights against budget owners and pipeline tags.
Tag pipelines with cost_center, product, or env so reports roll up the way finance expects.

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Getting started

1

Pin critical pipelines

Mark top-tier pipelines as favorites or critical so they appear on the default dashboard.
2

Enable baseline alerts

Start with failure notifications, then add duration and volume anomaly rules.
3

Review weekly

Block 15 minutes for DLQ trends and cost outliers before they become month-end surprises.