Data quality monitoring results system table reference

Beta

This feature is in Beta.

This page outlines the data quality monitoring results system table schema and includes sample queries. The table stores results of freshness and completeness checks, as well as downstream impact and root cause analysis, across all tables enabled for data quality monitoring in your metastore.

Table path: system.data_quality_monitoring.table_results

Only account admins can access this table, and they must grant access to others as needed. The system table uses default storage. Because it contains sample values and downstream usage data, use caution when granting access to others.

Data quality monitoring results table schema

The system.data_quality_monitoring.table_results table uses 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.

Example queries

Replace parameter values with your own before running.

Get all current incidents in a schema

SQL

WITH latest_rows AS (
    SELECT
        *,
        ROW_NUMBER() OVER (
            PARTITION BY table_id
            ORDER BY event_time DESC
        ) AS rn
    FROM
      system.data_quality_monitoring.table_results
    WHERE
      catalog_name = "c"
      AND schema_name = "s"
)

SELECT *
FROM latest_rows
WHERE
  rn = 1
  AND status = "Unhealthy"

Get all incident tables in a schema that have a high downstream impact

SQL

WITH latest_rows AS (
    SELECT
        *,
        ROW_NUMBER() OVER (
            PARTITION BY table_id
            ORDER BY event_time DESC
        ) AS rn
    FROM
      system.data_quality_monitoring.table_results
    WHERE
      catalog_name = "c"
      AND schema_name = "s"
)
SELECT *
FROM latest_rows
WHERE rn = 1
  AND downstream_impact.impact_level >= 3

Get all tables in a schema that are currently impacted by a freshness issue

SQL

WITH latest_rows AS (
    SELECT
        *,
        ROW_NUMBER() OVER (
            PARTITION BY table_id
            ORDER BY event_time DESC
        ) AS rn
    FROM
      system.data_quality_monitoring.table_results
    WHERE
      catalog_name = "c"
      AND schema_name = "s"
)

SELECT *
FROM latest_rows
WHERE rn = 1
  AND freshness.status = "Unhealthy"

Get all historical records for a table

SQL

SELECT *
FROM system.data_quality_monitoring.table_results
WHERE
  catalog_name = "c"
  AND schema_name = "s"
  AND table_name = "t"