check_results

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.	string
data_groups	Data groups list.	List[string]
data_group	Selected data group.	string
check_result_entries	Single check results	List[CheckResultEntryModel]

CheckResultStatus

Enumeration of check execution statuses. It is the highest severity or an error if the sensor could not be executed due to a configuration issue.

The structure of this object is described below

Data type	Enum values
string	valid warning execution_error error fatal

RuleSeverityLevel

Rule severity levels. Matches the severity level name (warning - 1, alert - 2, fatal - 3) with a numeric level.

The structure of this object is described below

Data type	Enum values
string	valid warning error fatal

CheckCurrentDataQualityStatusModel

The most recent data quality status for a single data quality check. If data grouping is enabled, this model will return the highest data quality issue status from all data quality results for all data groups.

The structure of this object is described below

Property name	Description	Data type
current_severity	The data quality issue severity for this data quality check. An additional value execution_error is used to tell that the check, sensor or rule failed to execute due to insufficient permissions to the table or an error in the sensor's template or a Python rule. For partitioned checks, it is the highest severity of all results for all partitions (time periods) in the analyzed time range.	CheckResultStatus
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
check_type	The check type: profiling, monitoring, partitioned.	CheckType
time_scale	The check time scale for monitoring and partitioned check types. The time scales are daily and monthly. The profiling checks do not have a time scale.	CheckTimeScale
category	Check category name, such as nulls, schema, strings, volume.	string
quality_dimension	Data quality dimension, such as Completeness, Uniqueness, Validity.	string

ColumnCurrentDataQualityStatusModel

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

The structure of this object is described below

Property name	Description	Data type
highest_severity_level	The severity of the highest identified data quality issue. This field will be null if no data quality checks were executed on the column.	CheckResultStatus
executed_checks	The total number of most recent checks that were executed on the column. 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 column must be updated.	integer
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]

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
connection_name	The connection name in DQOps.	string
schema_name	The schema name.	string
table_name	The table name.	string
highest_severity_level	The severity of the highest identified data quality issue. This field will be null if no data quality checks were executed on the table.	CheckResultStatus
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
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]