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Data quality monitoring

Beta

This feature is in Beta.

This page describes what data quality monitoring is, what it monitors, and how to use it. Data quality monitoring was formerly called anomaly detection.

To provide feedback on data quality monitoring, email lakehouse-monitoring-feedback@databricks.com.

What is data quality monitoring?

Using data quality monitoring, you can easily monitor the data quality of all of the tables in a schema. Databricks leverages data intelligence to automatically assess data quality, specifically evaluating the freshness and completeness of each table. Data owners have access to the logging table, which allows them can quickly identify and resolve anomalies across the entire metastore. Results at the catalog, schema, and table level are available in the Catalog Explorer or Governance Hub (preview).

Data quality monitoring does not modify any tables it monitors, nor does it add overhead to any jobs that populate these tables.

Requirements

  • Unity Catalog enabled workspace.
  • Existing users need serverless compute enabled. For instructions, see Connect to serverless compute.
  • To enable data quality monitoring on a schema, you must have MANAGE SCHEMA or MANAGE CATALOG privileges on the catalog schema.

How does data quality monitoring work?

Databricks creates a background job that monitors tables for freshness and completeness. Databricks uses smart scanning to determine when to scan tables.

Freshness refers to how recently a table has been updated. Data quality monitoring analyzes the history of commits to a table and builds a per-table model to predict the time of the next commit. If a commit is unusually late, the table is marked as stale.

note

Event freshness, which is based on event time columns and ingestion latency, was available only to legacy data quality monitoring users. In the current version, event freshness is not supported.

Completeness refers to the number of rows expected to be written to the table in the last 24 hours. Data quality monitoring analyzes the historical row count, and based on this data, predicts a range of expected number of rows. If the number of rows committed over the last 24 hours is less than the lower bound of this range, a table is marked as incomplete.

Enable data quality monitoring on a schema

To enable data quality monitoring on a schema, navigate to the schema in Unity Catalog.

  1. On the schema page, click the Details tab.

    Details tab for the schema page in Catalog Explorer.

  2. Click the Data Quality Monitoring toggle to enable it.

    Data quality monitor selector enabled.

  3. A scan is initiated. Databricks automatically scans each table at the same frequency it’s updated, providing up-to-date insights without requiring manual configuration for each table. On the initial scan for schemas enabled prior to September 24, 2025, Databricks ran the monitor on historical data ("backtesting") to check the quality of your tables as if data quality monitoring had been enabled on your schema two weeks ago.

  4. When the scan is complete, detected quality issues are logged in the output logging table with insights populated in the UI. You can access the UI at any time by clicking See results next to the Data Quality Monitoring toggle.

Data quality monitoring UI

important

On October 7, 2025, Databricks released a new version of data quality monitoring. Schemas enabled for data quality monitoring on or after that date have a new results UI.

For information about the new UI, see Data quality monitoring incidents UI.

For information about the legacy UI, see Data quality monitoring dashboard (legacy).

Databricks recommends that you enable the new version for all of your existing schemas.

To enable the new version, use the Data Quality Monitoring toggle to disable the feature, and then toggle again to re-enable it.

Data quality monitoring incidents UI

After you enable data quality monitoring on a schema, you can click "See results" to open the results page. You can also access the incidents view from Catalog Explorer or the Governance Hub (preview).

The incidents UI contains catalog and schema dropdowns. When you select a catalog, the schema dropdown is populated with schemas in that catalog that have data quality monitoring enabled.

  • If you have MANAGE or SELECT privileges on the catalog, you can view incidents at the catalog level.
  • To view incidents for a specific schema, you must also have MANAGE or SELECT privileges on that schema. Selecting a schema then shows incidents for just that schema.

The UI has two main sections:

  1. Summary section: Shows overall data quality for the selected scope, including the percentage of healthy tables and the percentage of schemas/tables currently monitored.
  2. Incident section: Lists incidents across all monitored tables in the selected scope. You can filter for incidents on high-impact tables determined using downstream impact analysis.

Each incident record includes the following details:

  • Reason: Whether the table is unhealthy due to freshness or completeness.
  • Since: The timestamp when the first incident was detected.
  • Impact: A qualitative measure of downstream impact (High, Medium, or Low), based on the number of downstream tables and queries affected.
  • Root cause: Information about upstream jobs contributing to the issue (see Review logged results for details).
  • Review: A link to the table quality page for more in-depth status and trend information.

Incidents UI showing summary, important incidents, and all incidents tabs.

Data quality monitoring dashboard (legacy)

note

The data quality monitoring dashboard was available only to legacy users. In the current version, use Data quality monitoring incidents UI.

The first data quality monitor run creates a dashboard to summarize results and trends derived from the logging table. The dashboard is automatically populated with insights for the scanned schema. A single dashboard is created per workspace at this path: /Shared/Databricks Quality Monitoring/Data Quality Monitoring.

Quality overview

The Quality Overview tab shows a summary of the latest quality status of tables in your schema based on the most recent evaluation.

To get started, you must enter the logging table for the schema you want to analyze to populate the dashboard.

The top section of the dashboard shows an overview of the results of the scan.

Data quality monitor schema summary in Quality Overview tab of the Dashboard.

Below the summary is a table listing quality incidents by impact. Any identified root causes are displayed in the root_cause_analysis column.

Quality incidents by impact in Quality Overview tab of the Dashboard.

Below the quality incident table is a table of identified static tables that have not been updated in a long time.

Table quality details

The Table Quality Details UI allows you to dive deeper into trends and analyze specific tables in your schema. You can access this view in several ways:

  • From the Incidents UI (new experience), by clicking on the review link in the incidents list.
  • From the Monitoring Dashboard (legacy Lakeview dashboard), by clicking on the table name in the Quality Overview tab.
  • From the UC Table viewer, by visiting the Quality tab on the table page.

All options take you to the same Table Quality Details view for the selected table.

Given a table, the UI shows summaries from each quality check for the table, with graphs of predicted and observed values at each evaluation timestamp. The graphs plot results from the last 1 week of data.

Table Quality Details UI for data quality monitor.

If the table failed the quality checks, the UI also displays any upstream jobs that were identified as the root cause.

Table Quality Details UI root cause table.

Set up alerts

To configure a Databricks SQL alert on the output results table, follow these steps in the Databricks alerts UI.

important

By default, only account admins can access the system table system.data_quality_monitoring.table_results. If other users need to configure alerts, make sure to grant appropriate access.

  1. Configure the query for the alert:

    SQL
    WITH rounded_data AS (
    SELECT
    DATE_TRUNC('HOUR', event_time) AS evaluated_at,
    CONCAT(catalog_name, '.', schema_name, '.', table_name) AS full_table_name,
    status,
    MAX(downstream_impact.num_queries_on_affected_tables) AS impacted_queries,
    MAX(freshness.commit_freshness.predicted_value) AS commit_expected,
    MAX(freshness.commit_freshness.last_value) AS commit_actual,
    MAX(completeness.daily_row_count.min_predicted_value) AS completeness_expected,
    MAX(completeness.daily_row_count.last_value) AS completeness_actual
    FROM system.data_quality_monitoring.table_results
    GROUP BY ALL
    )
    SELECT
    evaluated_at,
    full_table_name,
    status,
    commit_expected,
    commit_actual,
    completeness_expected,
    completeness_actual,
    impacted_queries
    FROM rounded_data
    WHERE
    evaluated_at >= current_timestamp() - INTERVAL 6 HOURS
    -- enter the minimum number of table violations before the alert is triggered
    AND impacted_queries > :min_tables_affected
    AND status = 'Unhealthy';
    note

    For legacy beta jobs, existing alert configuration should replace system.data_quality_monitoring.table_results with <catalog>.<schema>._quality_monitoring_summary.

  2. Configure the alert condition:

    Configure the trigger condition

  3. Customize the email template:

    Html
    <h4>The following tables are failing quality checks in the last hour</h4>

    <table>
    <tr>
    <td>
    <table>
    <tr>
    <th>Table</th>
    <th>Expected Staleness</th>
    <th>Actual Staleness</th>
    <th>Expected Row Volume</th>
    <th>Actual Row Volume</th>
    <th>Impact (queries)</th>
    </tr>
    {{#QUERY_RESULT_ROWS}}
    <tr>
    <td>{{full_table_name}}</td>
    <td>{{commit_expected}}</td>
    <td>{{commit_actual}}</td>
    <td>{{completeness_expected}}</td>
    <td>{{completeness_actual}}</td>
    <td>{{impacted_queries}}</td>
    </tr>
    {{/QUERY_RESULT_ROWS}}
    </table>
    </td>
    </tr>
    </table>

Now, you have an alert that triggers based on the downstream impact of the quality issue, helping you debug the table that triggered the alert.

Disable data quality monitoring

To disable data quality monitoring, click the Data Quality Monitoring toggle to disable it. Disabling data quality monitoring stops scanning tables in the schema for quality issues.

Data quality monitoring selector disabled.

Limitation

Data quality monitoring does not support the following:

  • Views or materialized views.
  • The determination of completeness does not take into account metrics such as the fraction of nulls, zero values, or NaN.

Advanced

Review logged results

By default, data quality monitoring scan results are stored in the system.data_quality_monitoring.table_results table. Only account admins can access this table, and they must grant access to others as needed. Data quality monitoring uses default storage to store the anomaly detection results. You are not billed for the storage.

important

The results table system.data_quality_monitoring.table_results contains all results across the entire metastore and includes sample values from tables in each catalog. Use caution when granting access to this table.

Each row in the results table corresponds to a single table in the schema that was scanned.

The table has the following schema:

Column name

Contents (for struct data type)

Data type

Description

Example data

event_time

timestamp

Time when the row was generated.

2025-06-27T12:00:00

catalog_name

string

Name of the catalog. Used to identify the table.

main

schema_name

string

Name of the schema. Used to identify the table.

default

table_name

string

Name of the table. Used to identify the table.

events

catalog_id

string

Stable ID for the catalog.

3f1a7d6e-9c59-4b76-8c32-8d4c74e289fe

schema_id

string

Stable ID for the schema.

3f1a7d6e-9c59-4b76-8c32-8d4c74e289fe

table_id

string

Stable ID for the table.

3f1a7d6e-9c59-4b76-8c32-8d4c74e289fe

status

string

Consolidated health status at the table level. "Unhealthy" if any check or group is unhealthy.

Healthy, Unhealthy, Unknown

freshness

struct

Freshness checks.

status

string

Overall freshness status.

Unhealthy

commit_freshness

struct

Commit freshness check results.

completeness

struct

Completeness check results.

status

string

Status of completeness check.

Unhealthy

total_row_count

struct

Total number of rows in the table over time.

daily_row_count

struct

Number of rows added each day.

downstream_impact

struct

Summary of downstream impact based on dependency graph.

impact_level

int

Severity indicator (0 = none, 1 = low, 2 = medium, 3 = high, 4 = very high).

2

num_downstream_tables

int

Number of downstream tables affected.

5

num_queries_on_affected_tables

int

Number of queries run on affected downstream tables over the last 30 days.

120

root_cause_analysis

struct

Information about upstream jobs contributing to the issue.

upstream_jobs

array

Metadata for each upstream job.

commit_freshness array structure

The commit_freshness struct contains the following:

Item name

Data type

Description

Example data

status

string

Status of commit freshness check.

Unhealthy

error_code

string

Error message encountered during check.

FAILED_TO_FIT_MODEL

last_value

timestamp

Last commit timestamp.

2025-06-27T11:30:00

predicted_value

timestamp

Predicted time by which the table should have been updated.

2025-06-27T11:45:00

total_row_count and daily_row_count array structure

The total_row_count and daily_row_count structs contain the following:

Item name

Data type

Description

Example data

status

string

Status of the check.

Unhealthy

error_code

string

Error message encountered during check.

FAILED_TO_FIT_MODEL

last_value

int

Number of rows observed in the last 24 hours.

500

min_predicted_value

int

Minimum expected number of rows in the last 24 hours.

10

max_predicted_value

int

Maximum expected number of rows in the last 24 hours.

1000

upstream_jobs array structure

The structure of the array shown in the upstream_jobs column is shown in the following table:

Item name

Data type

Description

Example data

job_id

string

Job ID.

12345

workspace_id

string

Workspace ID.

6051921418418893

job_name

string

Job display name.

daily_refresh

last_run_status

string

Status of the most recent run.

SUCCESS

run_page_url

string

URL of Databricks job run page.

https://.../runs/123

Downstream impact information

In the logged results table, the column downstream_impact is a struct with the following fields:

Field

Type

Description

impact_level

int

Integer value between 1 and 4 indicating the severity of the data quality issue. Higher values indicate greater disruption.

num_downstream_tables

int

Number of downstream tables that might be affected by the identified issue.

num_queries_on_affected_tables

int

Total number of queries that have referenced the affected and downstream tables in the past 30 days.

Set parameters for freshness and completeness evaluation (legacy)

Starting from July 21, 2025, configuration of the job parameters will not be supported for new customers. If you need to configure the job settings, please contact Databricks.

To edit the parameters that control the job, such as how often the job runs or the name of the logged results table, you must edit the job parameters on the Tasks tab of the job page.

Jobs page showing data quality monitoring job.

The following sections describe specific settings. For information about how to set task parameters, see Configure task parameters.

Schedule and notifications

To customize the schedule for the job, or to set up notifications, use the Schedules & Triggers settings on the jobs page. See Automating jobs with schedules and triggers.

Name of logging table

To change the name of the logging table, or save the table in a different schema, edit the job task parameter logging_table_name and specify the desired name. To save the logging table in a different schema, specify the full 3-level name.

Customize freshness and completeness evaluations

All of the parameters in this section are optional. By default, data quality monitoring determines thresholds based on an analysis of the table's history.

These parameters are fields inside the task parameter metric_configs. The format of metric_configs is a JSON string with the following default values:

JSON
[
{
"disable_check": false,
"tables_to_skip": null,
"tables_to_scan": null,
"table_threshold_overrides": null,
"table_latency_threshold_overrides": null,
"static_table_threshold_override": null,
"event_timestamp_col_names": null,
"metric_type": "FreshnessConfig"
},
{
"disable_check": true,
"tables_to_skip": null,
"tables_to_scan": null,
"table_threshold_overrides": null,
"metric_type": "CompletenessConfig"
}
]

The following parameters can be used for both freshness and completeness evaluations.

Field name

Description

Example

tables_to_scan

Only the specified tables are scanned.

["table_to_scan", "another_table_to_scan"]

tables_to_skip

The specified tables are skipped during the scan.

["table_to_skip"]

disable_check

Scan is not run. Use this parameter if you want to disable only the freshness scan or only the completeness scan.

true, false

The following parameters apply only to the freshness evaluation:

Field name

Description

Example

event_timestamp_col_names

List of timestamp columns tables in your schema might have. If a table has one of these columns, it is marked Unhealthy if the maximum value of this column is exceeded. Using this parameter might increase evaluation time and cost.

["timestamp", "date"]

table_threshold_overrides

A dictionary consisting of table names and thresholds (in seconds) that specify the maximum interval since the last table update before marking a table as Unhealthy.

{"table_0": 86400}

table_latency_threshold_overrides

A dictionary consisting of table names and latency thresholds (in seconds) that specify the maximum interval since the last timestamp in the table before marking a table as Unhealthy.

{"table_1": 3600}

static_table_threshold_override

Amount of time (in seconds) before a table is considered as a static table (that is, one that is no longer updated).

2592000

The following parameter applies only to the completeness evaluation:

Field name

Description

Example

table_threshold_overrides

A dictionary consisting of table names and row volume thresholds (specified as integers). If the number of rows added to a table over the previous 24 hours is less than the specified threshold, the table is marked Unhealthy.

{"table_0": 1000}