---
type: concept
x-llmwiki:
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  contentHash: ebea9d1d82600fa4ceb71e4daba497e521852e958fb6ce16da106ae28076306c
  pageDirectory: concepts
  sources:
    - anomaly-detection-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - unity-catalog-system-tables-for-data-quality
    - UCSTFDQ
  citations:
    - file: anomaly-detection-databricks-on-aws.md
    - file: anomaly-detection-databricks-on-aws.md
      start: 12
      end: 14
title: Unity Catalog System Tables for Data Quality
description: The `system.data_quality_monitoring.table_results` system table that stores all anomaly detection scan results, usable for building dashboards or setting up SQL alerts across the metastore.
tags:
  - system-tables
  - data-quality
  - unity-catalog
  - monitoring
timestamp: "2026-06-18T10:45:56.904Z"
---

---
title: Unity Catalog System Tables for Data Quality
summary: System tables that store data quality monitoring scan results, enabling analysis and alerting across the [Metastore](/concepts/metastore.md).
sources:
  - anomaly-detection-databricks-on-aws.md
kind: concept
createdAt: "2026-06-18T12:00:00.000Z"
updatedAt: "2026-06-18T12:00:00.000Z"
tags:
  - unity-catalog
  - data-quality
  - system-tables
  - anomaly-detection
  - databricks
aliases:
  - system.data_quality_monitoring
  - data quality system tables
confidence: 0.95
provenanceState: extracted
inferredParagraphs: 0
---

# Unity Catalog System Tables for Data Quality

**Unity Catalog System Tables for Data Quality** store the scan results generated by anomaly detection and other data quality monitoring features. These system tables allow you to analyze quality trends across all tables in a [Metastore](/concepts/metastore.md), build custom dashboards, and configure alerts outside the Catalog Explorer UI. ^[anomaly-detection-databricks-on-aws.md]

Only one system table is currently documented for data quality monitoring: `system.data_quality_monitoring.table_results`. ^[anomaly-detection-databricks-on-aws.md]

## `system.data_quality_monitoring.table_results`

The `system.data_quality_monitoring.table_results` system table contains the output of anomaly detection scans. It stores freshness, completeness, and percent-null evaluations for every monitored table in the [Metastore](/concepts/metastore.md). ^[anomaly-detection-databricks-on-aws.md]

Anomaly detection uses [default storage](/concepts/workspace-default-storage-path.md) to persist scan results in this table at no additional billing cost. ^[anomaly-detection-databricks-on-aws.md:12-14]

## Access control

By default, only [Account Admin (Unity Catalog)](/concepts/account-admin-unity-catalog.md) can access `system.data_quality_monitoring.table_results`. Account admins can grant `SELECT` on this table to other users or groups as needed. ^[anomaly-detection-databricks-on-aws.md]

For example, to allow a data engineering team to build dashboards on top of the data quality results:^[anomaly-detection-databricks-on-aws.md]

```sql
GRANT SELECT ON system.data_quality_monitoring.table_results TO `data-engineers`;
```

## Querying the table

You can query `system.data_quality_monitoring.table_results` just like any other table in Unity Catalog. The table contains one row per evaluation per table, with columns for the table name, schema, evaluation timestamp, metric type (freshness, completeness, percent-null), observed value, predicted range, and a verdict (healthy / unhealthy). ^[anomaly-detection-databricks-on-aws.md]

## Dashboard template

Databricks provides a dashboard template, `metastore-quality-dashboard.lvdash.json`, that visualises all quality results across the [Metastore](/concepts/metastore.md) using this system table. To use it:^[anomaly-detection-databricks-on-aws.md]

1. Ensure you have `SELECT` on `system.data_quality_monitoring.table_results`.
2. Download the template from the documentation.
3. In the workspace sidebar, click **Dashboards**.
4. From the **Create dashboard** drop-down, select **Import dashboard from file**.
5. Upload the downloaded `.lvdash.json` file.

The imported dashboard shows a summary of healthy tables, incident counts, and trends derived from the system table. ^[anomaly-detection-databricks-on-aws.md]

## Alerting

You can configure a [Databricks SQL Alert](/concepts/databricks-sql-alerts.md) on `system.data_quality_monitoring.table_results` to automatically notify teams when new anomalies are detected. Alerts can be set to trigger on unhealthy verdicts for specific schemas or tables. ^[anomaly-detection-databricks-on-aws.md]

## Related concepts

- [Anomaly Detection](/concepts/anomaly-detection.md) – The feature that populates the system tables with scan results.
- [Data Quality Monitoring](/concepts/data-quality-monitoring.md) – Broader framework for tracking table health.
- System tables (Unity Catalog) – Overview of all system tables available in Unity Catalog.
- [Account Admin (Unity Catalog)](/concepts/account-admin-unity-catalog.md) – The role that has default access to data quality system tables.
- [Default storage](/concepts/workspace-default-storage-path.md) – The storage location used for scan results.

## Sources

- anomaly-detection-databricks-on-aws.md

# Citations

1. [anomaly-detection-databricks-on-aws.md](/references/anomaly-detection-databricks-on-aws-a589cd11.md)
2. [anomaly-detection-databricks-on-aws.md:12-14](/references/anomaly-detection-databricks-on-aws-a589cd11.md)
