# Dataset Quality Statuses (SLA)

Mark the importance of each test on a dataset using **Minor**, **Major**, and **Critical** severities. The platform aggregates those severities together with the test pass / fail counts into a single dataset-level **SLA colour** (Green / Yellow / Red) that downstream BI reports can import directly.

## Setting severities

Severities are operator-set — the platform does not infer them.

1. Open the dataset's main page and select the **Test reports** tab.
2. Click on a job (a test result row) and, in the right-side panel, choose a severity — Minor, Major, or Critical.

Severities apply per-test, not per-dataset; one dataset can carry tests at all three severities. Each test's severity together with whether the latest run passed feeds the dataset's aggregate SLA colour.

## Importing SLA into BI reports

Each dataset exposes its current aggregate SLA at:

```
GET https://{platform_url}/api/datasets/{data_entity_id}/sla
```

`{data_entity_id}` is the numeric ID of the dataset's data entity — the same ID that appears in the dataset's catalog URL.

The endpoint returns a `DataSetSLAReport` that includes:

* The dataset's current **SLA colour** — `GREEN`, `YELLOW`, or `RED`.
* A breakdown of severity weights — counts of tests at each severity.
* A `slaRef` self-link.

BI tools can fetch this endpoint per dataset and render the colour as a one-glance trust signal next to dashboard tiles or report sections.

## How the SLA colour is computed

The SLA colour is **not** a direct severity-to-colour mapping — it is computed across the dataset's tests by `SLACalculator` based on which severities have failing tests:

* **Red** — at least one Critical test is failing, OR all Major-severity tests are failing, OR a particular all-but-one-Major-failing-and-all-Minors-failing pattern.
* **Yellow** — some Major tests failing (but not all), OR all Minor tests failing, OR no tests at any severity defined yet (the platform errs cautious when nothing is defined).
* **Green** — none of the above; passing tests dominate at every severity.

So changing a single test's severity from Major to Critical can flip the dataset from Yellow to Red without any test pass / fail status changing. Use severities to encode "how loud should this dataset's failure be" rather than as a labelling exercise.

## Where to next

* [Test Results Import](/features/data-quality/test-results-import.md) — how the test results that the SLA aggregates land in the catalog.
* [Quality Dashboard](/features/data-quality/dashboard.md) — the catalog-wide quality view that surfaces test outcomes alongside the per-dataset SLA.
* [Alerting](/features/active-platform-features/alerting.md) — DQ-test-failed alerts (which fire on the test-result event the SLA also consumes).
* [Visibility for Data Quality Engineer](/use-cases/use-cases/dq-visibility.md) — the DQ-engineer use case end-to-end.


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