> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vortexiq.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# DLT Pipeline Status Distribution, Databricks

> DLT Pipeline Status Distribution for Databricks workspaces. Tracked live in Vortex IQ Nerve Centre. How to read it, why it matters, and how to act on it.

**Card class:** [Non-Hero](/nerve-centre/overview#card-classes-explained)  •  **Category:** [Pipelines](/nerve-centre/connectors#connectors-by-type)

## At a glance

> A real-time donut showing how your Delta Live Tables (DLT) pipelines are split across Running, Idle, Failed, and Stopped states. It is the one-glance answer to "are my declarative pipelines healthy right now?" Any slice in the Failed segment means a pipeline is not producing fresh data and a downstream table is going stale.

## What it tracks

The card reads the pipeline inventory from the Databricks DLT API (`GET /pipelines/list`, the Lakeflow Declarative Pipelines list endpoint) and buckets every pipeline by its current state: **Running** (an update is actively processing), **Idle** (healthy but between scheduled or continuous updates), **Failed** (the last update ended in error), and **Stopped** (paused or never started). The donut shows the proportion in each state in real time, refreshed every polling cycle, so a pipeline flipping into Failed appears within one cycle.

A healthy distribution is mostly Idle and Running with an empty Failed segment. The states that demand attention are Failed (a broken pipeline, stale downstream tables) and unexpectedly Stopped (a pipeline that should be scheduled but is not). Because DLT pipelines are distinct from classic Jobs, this card is the streaming / declarative counterpart to [Job Success Rate (24h)](/nerve-centre/kpi-cards/databricks/job-success-rate-24h); read both for full pipeline coverage. When a slice turns Failed, pair with [Pipeline Lag (since last success)](/nerve-centre/kpi-cards/databricks/pipeline-lag-since-last-success) to size how stale the data has become and [Failed Jobs (24h)](/nerve-centre/kpi-cards/databricks/failed-jobs-24h) to check whether the breakage spans both pipeline types.

This card has no hard alert threshold; it is a live status overview rather than a trigger. For the alerting view on data freshness, use the lag and failed-job cards.

## Reconciling against the source

Open **Workflows → Delta Live Tables (Pipelines)** in the Databricks workspace: the pipeline list shows each pipeline's latest update state, which should match the donut's segments. For an exact count, query `system.lakeflow.pipelines` in a SQL warehouse. Brief differences are normal because the API reflects state within one polling cycle while the UI updates on page refresh.

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### Tracked live in Vortex IQ Nerve Centre

*DLT Pipeline Status Distribution* is one of hundreds of KPI pulses Vortex IQ tracks across Databricks and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English.

[Start for free](https://app.vortexiq.ai/login) or [book a demo](https://www.vortexiq.ai/contact-us) to see this metric running on your own data.