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Last updated: September 02, 2024

DQOps REST API default_table_check_patterns models reference

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

TargetTablePatternSpec

The configuration of a table pattern to match default table checks.

The structure of this object is described below

 Property name   Description                       Data type 
connection The data source connection name filter. Accepts wildcards in the format: conn, , conn*. string
schema The schema name filter. Accepts wildcards in the format: _prod, , pub*. string
table The table name filter. Accepts wildcards in the format: _customers, , fact_*. string
stage The table stage filter. Accepts wildcards in the format: _landing, , staging_*. string
table_priority The maximum table priority (inclusive) for tables that are covered by the default checks. integer
label The label filter. Accepts wildcards in the format: _customers, , fact_*. The label must be present on the connection or table. string

DefaultTableChecksPatternListModel

The listing model of table-level default check patterns that is returned by the REST API.

The structure of this object is described below

 Property name   Description                       Data type 
pattern_name Pattern name. string
priority The priority of the pattern. Patterns with lower values are applied before patterns with higher priority values. integer
disabled Disables this data quality check configuration. The checks will not be activated. boolean
description The description (documentation) of this data quality check configuration. string
target_table The filters for the target table. TargetTablePatternSpec
can_edit Boolean flag that decides if the current user can update or delete this object. boolean
yaml_parsing_error Optional parsing error that was captured when parsing the YAML file. This field is null when the YAML file is valid. If an error was captured, this field returns the file parsing error message and the file location. string

TableMonitoringCheckCategoriesSpec

Container of table level monitoring, divided by the time window (daily, monthly, etc.)

The structure of this object is described below

 Property name   Description                       Data type 
daily Configuration of daily monitoring evaluated at a table level. TableDailyMonitoringCheckCategoriesSpec
monthly Configuration of monthly monitoring evaluated at a table level. TableMonthlyMonitoringCheckCategoriesSpec

TablePartitionedCheckCategoriesSpec

Container of table level partitioned checks, divided by the time window (daily, monthly, etc.)

The structure of this object is described below

 Property name   Description                       Data type 
daily Configuration of day partitioned data quality checks evaluated at a table level. TableDailyPartitionedCheckCategoriesSpec
monthly Configuration of monthly partitioned data quality checks evaluated at a table level.. TableMonthlyPartitionedCheckCategoriesSpec

TableDefaultChecksPatternSpec

The default configuration of table-level data quality checks that are enabled as data observability checks to analyze basic measures and detect anomalies on tables. This configuration serves as a data quality policy that defines the data quality checks that are verified on matching tables.

The structure of this object is described below

 Property name   Description                       Data type 
priority The priority of the pattern. Patterns with lower values are applied before patterns with higher priority values. integer
disabled Disables this data quality check configuration. The checks will not be activated. boolean
description The description (documentation) of this data quality check configuration. string
target The target table filter that are filtering the table and connection on which the default checks are applied. TargetTablePatternSpec
profiling_checks Configuration of data quality profiling checks that are enabled. Pick a check from a category, apply the parameters and rules to enable it. TableProfilingCheckCategoriesSpec
monitoring_checks Configuration of table level monitoring checks. Monitoring checks are data quality checks that are evaluated for each period of time (daily, weekly, monthly, etc.). A monitoring check stores only the most recent data quality check result for each period of time. TableMonitoringCheckCategoriesSpec
partitioned_checks Configuration of table level date/time partitioned checks. Partitioned data quality checks are evaluated for each partition separately, raising separate alerts at a partition level. The table does not need to be physically partitioned by date, it is possible to run data quality checks for each day or month of data separately. TablePartitionedCheckCategoriesSpec

DefaultTableChecksPatternModel

Default table-level checks pattern model that is returned by the REST API. Describes a configuration of data quality checks for a named pattern. DQOps applies these checks on tables that match the filter.

The structure of this object is described below

 Property name   Description                       Data type 
pattern_name Pattern name string
pattern_spec The default checks specification. TableDefaultChecksPatternSpec
can_edit Boolean flag that decides if the current user can update or delete this object. boolean
yaml_parsing_error Optional parsing error that was captured when parsing the YAML file. This field is null when the YAML file is valid. If an error was captured, this field returns the file parsing error message and the file location. string