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The Pipeline tab of the Observability Dashboard tracks the health of the pipelines and models that move and transform data — how often runs succeed, how long they take, why they fail, and which ones are slowest. Navigate to Analytics → Observability (Data) → Pipeline to access it.

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AI Summary

Same AI Summary component as the other Analytics tabs. In validation it sometimes continued to show the Data tab’s summary text immediately after switching to Pipeline — give it a moment to refresh, or rely on the tiles and widgets below if it looks out of date.

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KPI cards

All three tiles animate with a count-up effect and can take several seconds to settle. Read a tile only after two consecutive looks agree.

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Widgets

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Success rate trend

A line chart of pipeline success rate (0-100%) over the selected date range, time-bucketed. In validation, this trend swung widely over the 90-day window (from under 20% up to roughly 80%), making this one of the more volatile trend widgets on any Analytics dashboard — worth checking before assuming a flat or near-100% baseline for your own pipelines.

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Failure reasons

A horizontal bar chart ranking failure causes by count (e.g. “Other failure”, “Execution error”). Use this to see at a glance which failure category dominates before drilling into individual runs.

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Pipeline run outcome mix

A stacked bar chart of run outcomes (Success, Failed, Skipped) per time bucket across the date range. Unlike the Success rate trend’s line, this shows absolute run volume per bucket, not just a rate — useful for distinguishing “the success rate dropped because of a few extra failures” from “the success rate dropped because run volume spiked.”

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Slowest pipelines and models

A table ranking pipelines and models by average duration. Columns: Pipeline / Model, Duration Trend (a sparkline plus the delta vs. the start of the sparkline window), Avg. Duration, Status (Healthy or Breaching — whether the pipeline is currently within its configured SLA duration threshold).

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Filters

Same filter bar as the other Analytics dashboards: Domain, Application, Product, Tag, Source, Asset Type, Asset, Date Range, Slice By, Previous Period. See Catalog Dashboard for the full list of options and the general filtering mechanism, which is identical here — applying a filter re-scopes every tile and widget to only the matching pipelines/runs.
Filter selections persist across page reloads and across navigating away and back, for as long as your session is active. Click Reset to confirm you’re looking at the true unfiltered baseline before drawing conclusions from the numbers.

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Slice By

Slice By (Day, Week, Month, Quarter, Half-year, Year; defaults to Week) rebuckets the Success rate trend and Pipeline run outcome mix charts’ time axis, the same way it rebuckets the Quality Dashboard’s trend chart and the Observability Data tab’s heatmaps.
This dashboard’s Slice-By behavior was not independently re-verified click-by-click during this validation pass — it follows directly from the pattern confirmed on three other Analytics dashboards (Quality, Catalog, Observability → Data): Slice By only rebuckets time-series visualizations and never changes a KPI tile. If you see different behavior in your environment, treat the live dashboard as the source of truth and let the docs team know via the feedback link so this page can be corrected.

Observability Dashboard — Data

Freshness, volume, and schema reliability for assets — the data-side counterpart to this pipeline-side dashboard

Observability Dashboard — Report

Report and dashboard health, staleness, and usage