Last updated: July 22, 2025

DQOps REST API check_results models reference

The references of all objects used by check_results REST API operations are listed below.

CheckResultsDetailedLoadMode

The mode of loading a list of detailed check results: the most recent values for each data group, or all results of the first group.

The structure of this object is described below

Data type	Enum values
string	first_data_group most_recent_per_group

---

CheckResultsListModel

Check detailed results. Returned in the context of a single data group, with a supplied list of other data groups.

The structure of this object is described below

Property name	Description	Data type
check_hash	Check hash	long
check_category	Check category name	string
check_name	Check name	string
check_display_name	Check display name	string
check_type	Check type	CheckType
data_groups	Data groups list	List[string]
data_group	Selected data group	string
check_result_entries	Single check results	List[CheckResultEntryModel]

---

TableCurrentDataQualityStatusModel

The table validity status. It is a summary of the results of the most recently executed data quality checks on the table.

The structure of this object is described below

Property name	Description	Data type
data_domain	Data domain name.	string
connection_name	The connection name in DQOps.	string
schema_name	The schema name.	string
table_name	The table name.	string
total_row_count	Most recent row count. Returned only when the status of the monitoring or profiling checks was requested.	long
data_freshness_delay_days	The last measured data freshness delay in days. Requires any of the data freshness checks in the monitoring section configured and up to date.	double
current_severity	The most recent data quality issue severity for this table. When the table is monitored using data grouping, it is the highest issue severity of all recently analyzed data groups. For partitioned checks, it is the highest severity of all results for all partitions (time periods) in the analyzed time range.	RuleSeverityLevel
highest_historical_severity	The highest severity of previous executions of this data quality issue in the analyzed time range. It can be different from the current_severity if the data quality issue was solved and the most recently data quality issue did not detect it anymore. For partitioned checks, this field returns the same value as the current_severity, because data quality issues in older partitions are still valid.	RuleSeverityLevel
executed_checks	The total number of most recent checks that were executed on the table. Table comparison checks that are comparing groups of data are counted as the number of compared data groups.	integer
valid_results	The number of most recent valid data quality checks that passed without raising any issues.	integer
warnings	The number of most recent data quality checks that failed by raising a warning severity data quality issue.	integer
errors	The number of most recent data quality checks that failed by raising an error severity data quality issue.	integer
fatals	The number of most recent data quality checks that failed by raising a fatal severity data quality issue.	integer
execution_errors	The number of data quality check execution errors that were reported due to access issues to the data source, invalid mapping in DQOps, invalid queries in data quality sensors or invalid python rules. When an execution error is reported, the configuration of a data quality check on a table must be updated.	integer
data_quality_kpi	Data quality KPI score for the table, measured as a percentage of passed data quality checks. DQOps counts data quality issues at a warning severity level as passed checks. The data quality KPI score is a value in the range 0..100.	double
checks	The dictionary of statuses for data quality checks. The keys are data quality check names, the values are the current data quality check statuses that describe the most current status.	Dict[string, CheckCurrentDataQualityStatusModel]
columns	Dictionary of data statues for all columns that have any known data quality results. The keys in the dictionary are the column names.	Dict[string, ColumnCurrentDataQualityStatusModel]
dimensions	Dictionary of the current data quality statues for each data quality dimension.	Dict[string, DimensionCurrentDataQualityStatusModel]
table_exist	The flag informing whether the table exist. The table might not exist for the imported data lineage source tables.	boolean

---