Manage data quality rules

Create data quality rules

Data quality rules can be created using three different creator methods depending on the type of rule you want to create:

1. Column level rules: For rules that apply to specific columns (for example, Freshness, Null Count)
2. Table level rules: For rules that apply to entire tables (for example, Row Count)
3. Custom SQL rules: For Custom SQL rule only

Column level rules

Column level rules are used for data quality checks that apply to specific columns within a asset.

Java

Coming soon

Python

Create a column level data quality rule

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import DataQualityRule, Table, Column
from pyatlan.model.enums import (
    DataQualityRuleAlertPriority,
    DataQualityRuleTemplateType,
    DataQualityRuleThresholdCompareOperator,
    DataQualityDimension,
    DataQualityRuleThresholdUnit
)

client = AtlanClient()

# Create a Freshness rule for a specific column
dq_rule = DataQualityRule.column_level_rule_creator( # (1)
    client=client, # (2)
    rule_type=DataQualityRuleTemplateType.FRESHNESS, # (3)
    asset=Table.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq/weather/monitoring"), # (4)
    column=Column.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq/weather/monitoring/evaluated_at"), # (5)
    threshold_value=1, # (6)
    alert_priority=DataQualityRuleAlertPriority.URGENT, # (7)
    threshold_unit=DataQualityRuleThresholdUnit.DAYS # (8)
)

response = client.asset.save(dq_rule) # (9)

# Create a Null Count rule for a specific column
dq_rule_null = DataQualityRule.column_level_rule_creator( 
    client=client,
    rule_type=DataQualityRuleTemplateType.NULL_COUNT,
    asset=Table.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq/weather/monitoring"),
    column=Column.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq_poc/accuweather/_quality_monitoring_summary/catalog"),
    threshold_compare_operator=DataQualityRuleThresholdCompareOperator.LESS_THAN_EQUAL, # (10)
    threshold_value=5, 
    alert_priority=DataQualityRuleAlertPriority.URGENT,
    row_scope_filtering_enabled=True # (11)
)

response = client.asset.save(dq_rule_null)

1. Use the column_level_rule_creator method to create column-level data quality rules.
2. Provide the Atlan client instance.
3. Specify the rule type using the DataQualityRuleTemplateType enum (for example, DataQualityRuleTemplateType.FRESHNESS, DataQualityRuleTemplateType.NULL_COUNT).
4. Reference the asset using its qualified name to which you want to apply this rule.
5. Reference the specific column using its qualified name of that asset to which you want to apply this rule. Make sure the column data type is compatible with the rule type (for example, date/time columns for Freshness rules).
6. Set the threshold value for the rule (same as you would in the UI).
7. Set the alert priority level (same as you would in the UI).
8. Optional: Specify the threshold unit (for example, DAYS, HOURS) for rules that support units (such as Freshness). For rules without units (such as Null Count), omit this parameter.
9. Save the data quality rule to Atlan.
10. Optional: Specify the threshold compare operator (same as you would in the UI).
11. Optional: Set row_scope_filtering_enabled=True to enable incremental data quality monitoring (check UI for availability).
12. Optional: Pass rule_conditions parameter for rule types that support conditions (for example, String Length, Regex, Valid Values, Reconciliation rules).

Kotlin

Coming soon

Raw REST API

Coming soon

Rule types with conditions

These rule types (String Length, Regex, Valid Values) make data validation faster and easier by providing common checks that previously required custom SQL or complex setup. They support adding rule conditions along with thresholds.

Java

Coming soon

Python

Create advanced data quality rules

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import DataQualityRule, Table, Column
from pyatlan.model.enums import (
    DataQualityRuleAlertPriority,
    DataQualityRuleTemplateType,
    DataQualityRuleTemplateConfigRuleConditions
)
from pyatlan.model.dq_rule_conditions import DQRuleConditionsBuilder

client = AtlanClient()

# Create a String Length rule with conditions
rule_conditions = ( # (1)
    DQRuleConditionsBuilder()
    .add_condition(
        type=DataQualityRuleTemplateConfigRuleConditions.STRING_LENGTH_BETWEEN,
        min_value=5,
        max_value=50,
    )
    .build()
)

dq_rule_string = DataQualityRule.column_level_rule_creator( # (2)
    client=client,
    rule_type=DataQualityRuleTemplateType.STRING_LENGTH,
    asset=Table.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq/weather/monitoring"),
    column=Column.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq/weather/monitoring/country"),
    threshold_value=6,
    alert_priority=DataQualityRuleAlertPriority.URGENT,
    rule_conditions=rule_conditions,
    row_scope_filtering_enabled=True
)

response = client.asset.save(dq_rule_string) # (3)

# Create a Regex rule with pattern validation
rule_conditions_regex = ( # (4)
    DQRuleConditionsBuilder()
    .add_condition(
        type=DataQualityRuleTemplateConfigRuleConditions.REGEX_NOT_MATCH,
        value="^[A-Za-z]+$",
    )
    .build()
)

dq_rule_regex = DataQualityRule.column_level_rule_creator( # (5)
    client=client,
    rule_type=DataQualityRuleTemplateType.REGEX_MATCH,
    asset=Table.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq/weather/monitoring"),
    column=Column.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq/weather/monitoring/country"),
    threshold_value=2,
    alert_priority=DataQualityRuleAlertPriority.URGENT,
    rule_conditions=rule_conditions_regex,
    row_scope_filtering_enabled=True
)

response = client.asset.save(dq_rule_regex) # (6)

# Create a Valid Values rule with allowed values list
rule_conditions_valid = ( # (7)
    DQRuleConditionsBuilder()
    .add_condition(
        type=DataQualityRuleTemplateConfigRuleConditions.IN_LIST,
        value=["United States", "Canada", "Mexico"],
    )
    .build()
)

dq_rule_valid = DataQualityRule.column_level_rule_creator( # (8)
    client=client,
    rule_type=DataQualityRuleTemplateType.VALID_STRING_VALUES,
    asset=Table.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq/weather/monitoring"),
    column=Column.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq/weather/monitoring/country"),
    threshold_value=2,
    alert_priority=DataQualityRuleAlertPriority.URGENT,
    rule_conditions=rule_conditions_valid,
    row_scope_filtering_enabled=True
)

response = client.asset.save(dq_rule_valid) # (9)

# Create a Valid Values Reference rule using a reference table/column
rule_conditions_reference = ( # (10)
    DQRuleConditionsBuilder()
    .add_condition(
        type=DataQualityRuleTemplateConfigRuleConditions.IN_LIST_REFERENCE,
        reference_table="default/databricks/1750768309/dq/weather/valid_countries",
        reference_column="default/databricks/1750768309/dq/weather/valid_countries/country_code",
    )
    .build()
)

dq_rule_reference = DataQualityRule.column_level_rule_creator( # (11)
    client=client,
    rule_type=DataQualityRuleTemplateType.VALID_STRING_VALUES_REFERENCE,
    asset=Table.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq/weather/monitoring"),
    column=Column.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq/weather/monitoring/country"),
    threshold_value=2,
    alert_priority=DataQualityRuleAlertPriority.URGENT,
    rule_conditions=rule_conditions_reference,
    row_scope_filtering_enabled=True
)

response = client.asset.save(dq_rule_reference) # (12)

1. Create rule conditions using DQRuleConditionsBuilder with String Length condition type
2. Create a String Length rule with conditions using DataQualityRuleTemplateType.STRING_LENGTH
3. Save the String Length rule to Atlan
4. Create rule conditions for Regex pattern validation
5. Create a Regex rule using DataQualityRuleTemplateType.REGEX_MATCH
6. Save the Regex rule to Atlan
7. Create rule conditions for Valid Values with an allowed values list
8. Create a Valid Values rule using DataQualityRuleTemplateType.VALID_STRING_VALUES
9. Save the Valid Values rule to Atlan
10. Create rule conditions for Valid Values Reference using a reference table and column
11. Create a Valid Values Reference rule using DataQualityRuleTemplateType.VALID_STRING_VALUES_REFERENCE
12. Save the Valid Values Reference rule to Atlan

Kotlin

Coming soon

Raw REST API

Coming soon

Reconciliation rules

Reconciliation rules allow you to compare metrics between a base table/column and a target table/column. These rules support reconciliation conditions that specify the target table and column for comparison.

Java

Coming soon

Python

Create reconciliation data quality rules

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import DataQualityRule, Table, Column, View
from pyatlan.model.enums import (
    DataQualityRuleAlertPriority,
    DataQualityRuleTemplateType,
    DataQualityRuleTemplateConfigRuleConditions,
    DataQualityRuleThresholdCompareOperator,
    DataQualityRuleThresholdUnit
)
from pyatlan.model.dq_rule_conditions import DQRuleConditionsBuilder

client = AtlanClient()

# Create a Row Count Reconciliation rule at table level
rule_conditions_recon = ( # (1)
    DQRuleConditionsBuilder()
    .add_condition(
        type=DataQualityRuleTemplateConfigRuleConditions.ROW_COUNT_RECON,
        target_table="default/snowflake/1755428756/COVID19_DATASET_PROD/small_case/NYT_US_COVID19",
    )
    .build()
)

dq_rule_recon = DataQualityRule.table_level_rule_creator( # (2)
    client=client,
    rule_type=DataQualityRuleTemplateType.RECON_ROW_COUNT,
    asset=View.ref_by_qualified_name(
        qualified_name="default/snowflake/1755428756/COVID19_DATASET_PROD/PUBLIC/CDC_INPATIENT_BEDS_ALL_VIEW"
    ),
    threshold_value=3,
    threshold_unit=DataQualityRuleThresholdUnit.PERCENTAGE,
    alert_priority=DataQualityRuleAlertPriority.NORMAL,
    rule_conditions=rule_conditions_recon, # (3)
)

response = client.asset.save(dq_rule_recon) # (4)

# Create a Unique Count Reconciliation rule at column level
rule_conditions_unique_recon = ( # (5)
    DQRuleConditionsBuilder()
    .add_condition(
        type=DataQualityRuleTemplateConfigRuleConditions.UNIQUE_COUNT_RECON,
        target_table="default/snowflake/1755428756/COVID19_DATASET_PROD/small_case/KFF_HCP_CAPACITY",
        target_column="default/snowflake/1755428756/COVID19_DATASET_PROD/small_case/KFF_HCP_CAPACITY/TOTAL_CHCS",
    )
    .build()
)

dq_rule_unique_recon = DataQualityRule.column_level_rule_creator( # (6)
    client=client,
    rule_type=DataQualityRuleTemplateType.RECON_UNIQUE_COUNT,
    asset=View.ref_by_qualified_name(
        qualified_name="default/snowflake/1755428756/COVID19_DATASET_PROD/PUBLIC/CDC_INPATIENT_BEDS_ALL_VIEW"
    ),
    column=Column.ref_by_qualified_name(
        qualified_name="default/snowflake/1755428756/COVID19_DATASET_PROD/PUBLIC/CDC_INPATIENT_BEDS_ALL_VIEW/INPATIENT_BEDS_OCCUPIED"
    ),
    threshold_value=2,
    threshold_unit=DataQualityRuleThresholdUnit.PERCENTAGE,
    alert_priority=DataQualityRuleAlertPriority.NORMAL,
    rule_conditions=rule_conditions_unique_recon,
    row_scope_filtering_enabled=True # (7)
)

response = client.asset.save(dq_rule_unique_recon) # (8)

1. Create reconciliation rule conditions. For ROW_COUNT_RECON, only target_table is required. For other reconciliation types (AVERAGE_RECON, SUM_RECON, DUPLICATE_COUNT_RECON, UNIQUE_COUNT_RECON), both target_table and target_column are required.
2. Create a Row Count Reconciliation rule using DataQualityRuleTemplateType.RECON_ROW_COUNT at the table level. The threshold_compare_operator is optional and defaults to the template config value if not provided.
3. Pass rule_conditions as a parameter to table_level_rule_creator.
4. Save the reconciliation rule to Atlan.
5. Create reconciliation rule conditions for column-level rules. Both target_table and target_column are required.
6. Create a Unique Count Reconciliation rule using DataQualityRuleTemplateType.RECON_UNIQUE_COUNT at the column level.
7. Optional: Enable row scope filtering for reconciliation rules. When enabled, the system validates that both the base asset and target table have row scope filter columns configured.
8. Save the reconciliation rule to Atlan.

Kotlin

Coming soon

Raw REST API

Coming soon

Table Level rules

Table level rules are used for data quality checks that apply to entire table.

Java

Coming soon

Python

Create a Table Level data quality rule

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import DataQualityRule, Table
from pyatlan.model.enums import (
    DataQualityRuleAlertPriority,
    DataQualityRuleTemplateType,
    DataQualityRuleThresholdCompareOperator
)

client = AtlanClient()

# Create a Row Count rule for a table
dq_rule = DataQualityRule.table_level_rule_creator( # (1)
    client=client, # (2)
    rule_type=DataQualityRuleTemplateType.ROW_COUNT, # (3)
    asset=Table.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq_poc/accuweather/_quality_monitoring_summary"), # (4)
    threshold_compare_operator=DataQualityRuleThresholdCompareOperator.EQUAL, # (5)
    threshold_value=15, # (6)
    alert_priority=DataQualityRuleAlertPriority.URGENT # (7)
)

response = client.asset.save(dq_rule) # (8)

1. Use the table_level_rule_creator method to create table-level data quality rules.
2. Provide the Atlan client instance.
3. Specify the rule type using the DataQualityRuleTemplateType enum (for example, DataQualityRuleTemplateType.ROW_COUNT).
4. Reference the asset using its qualified name to which you want to apply this rule.
5. Optional: Set the threshold comparison operator (for example, EQUAL, LESS_THAN_EQUAL).
6. Set the threshold value for the rule (same as you would in the UI).
7. Set the alert priority level (same as you would in the UI).
8. Save the data quality rule to Atlan.

Kotlin

Coming soon

Raw REST API

Coming soon

Custom SQL rule

Custom SQL rule allow you to define data quality check using custom SQL queries.

Java

Coming soon

Python

Create a Custom SQL data quality rule

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import DataQualityRule, Table
from pyatlan.model.enums import (
    DataQualityRuleAlertPriority,
    DataQualityRuleCustomSQLReturnType,
    DataQualityRuleThresholdCompareOperator,
    DataQualityDimension
)

client = AtlanClient()

# Create a Custom SQL rule
dq_rule = DataQualityRule.custom_sql_creator( # (1)
    client=client, # (2)
    rule_name="Test SQL Rule", # (3)
    asset=Table.ref_by_qualified_name(qualified_name="default/databricks/1750768309/dq_poc/accuweather/_quality_monitoring_summary"), # (4)
    custom_sql="SELECT count(*) FROM `dq_poc`.`accuweather`.`_quality_monitoring_summary`", # (5)
    threshold_compare_operator=DataQualityRuleThresholdCompareOperator.LESS_THAN_EQUAL, # (6)
    threshold_value=10, # (7)
    alert_priority=DataQualityRuleAlertPriority.URGENT, # (8)
    dimension=DataQualityDimension.COMPLETENESS, # (9)
    custom_sql_return_type=DataQualityRuleCustomSQLReturnType.ROW_COUNT, # (10)
    description="Custom SQL rule for completeness check" # (11)
)

response = client.asset.save(dq_rule) # (12)

1. Use the custom_sql_creator method to create custom SQL data quality rules.
2. Provide the Atlan client instance.
3. Provide a name for the custom rule (same as you would in the UI).
4. Reference the asset using its qualified name to which you want to apply this rule.
5. Provide the custom SQL query for the rule (same as you would in the UI).
6. Set the threshold comparison operator (same as you would in the UI).
7. Set the threshold value for the rule (same as you would in the UI).
8. Set the alert priority level (same as you would in the UI).
9. Set the data quality dimension (for example, COMPLETENESS, ACCURACY) (same as you would in the UI).
10. Optional: Specify the return type of the custom SQL query (ROW_COUNT or NUMERIC_VALUE). This indicates whether the SQL returns a row count or a numeric value.
11. Optional: Provide a description for the rule.
12. Save the data quality rule to Atlan.

Kotlin

Coming soon

Raw REST API

Coming soon

Update data quality rules

To update an existing data quality rule, you only need to provide the qualified name and the Atlan Client. All other parameters are optional and will only be updated if provided.

Java

Coming soon

Python

Update a data quality rule

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import DataQualityRule
from pyatlan.model.enums import (
    DataQualityRuleAlertPriority,
    DataQualityRuleCustomSQLReturnType,
    DataQualityRuleThresholdCompareOperator,
    DataQualityDimension,
    DataQualityRuleThresholdUnit,
    DataQualityRuleTemplateConfigRuleConditions
)
from pyatlan.model.dq_rule_conditions import DQRuleConditionsBuilder

client = AtlanClient()

# Update specific fields of an existing data quality rule
updated_rule = DataQualityRule.updater( # (1)
    client=client, # (2)
    qualified_name="default/databricks/1750768309/dq_poc/accuweather/_quality_monitoring_summary/rule/40e01c39-dcb8-4348-9259-041f353a8348", # (3)
    threshold_compare_operator=DataQualityRuleThresholdCompareOperator.LESS_THAN_EQUAL, # (4) 
    threshold_value=20, # (5)
    alert_priority=DataQualityRuleAlertPriority.HIGH, # (6)
    threshold_unit=DataQualityRuleThresholdUnit.DAYS, # (7)
    dimension=DataQualityDimension.COMPLETENESS, # (8)
    custom_sql="SELECT count(*) FROM updated_table", # (9)
    custom_sql_return_type=DataQualityRuleCustomSQLReturnType.NUMERIC_VALUE, # (10)
    rule_name="Updated Rule Name", # (11)
    description="Updated description for the rule" # (12)
)

response = client.asset.save(updated_rule)

# Update rule conditions and enable row filtering for a String Length rule
updated_rule_conditions = (
    DQRuleConditionsBuilder()
    .add_condition(
        type=DataQualityRuleTemplateConfigRuleConditions.STRING_LENGTH_BETWEEN,
        min_value=10,
        max_value=100,
    )
    .build()
)

updated_string_rule = DataQualityRule.updater(
    client=client,
    qualified_name="default/databricks/1750768309/dq/weather/monitoring/rule/40e01c39-dcb8-4348-9259-041f353a8348",
    threshold_value=15,
    alert_priority=DataQualityRuleAlertPriority.HIGH,
    rule_conditions=updated_rule_conditions, # (13)
    row_scope_filtering_enabled=True # (14)
)

response = client.asset.save(updated_string_rule) # (15)

1. Use the updater method to update an existing data quality rule.
2. Provide the Atlan client instance.
3. Provide the qualified name of the existing rule.
4. Optional: Update the threshold comparison operator.
5. Optional: Update the threshold value for the rule.
6. Optional: Update the alert priority level.
7. Optional: Update the threshold unit.
8. Optional: Update the data quality dimension (for custom SQL rules).
9. Optional: Update the custom SQL query (for custom SQL rules).
10. Optional: Update the custom SQL return type (ROW_COUNT or NUMERIC_VALUE) for custom SQL rules.
11. Optional: Update the name of the rule (for custom SQL rules).
12. Optional: Update the description of the rule (for custom SQL rules).
13. Optional: Update the rule conditions (check UI for availability).
14. Optional: Enable row scope filtering (check UI for availability).
15. Save the updated data quality rule to Atlan.

Kotlin

Coming soon

Raw REST API

Coming soon

UI compatibility required

When updating data quality rules, only update parameters that are applicable to your specific rule type as shown in the UI. Updating parameters that don't apply to your rule type may cause the operation to fail or produce unexpected results.

Retrieve data quality rules

To retrieve data quality rules, you can use fluent search to retireve data quality rules.

Java

Coming soon

Python

Retrieve data quality rules

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import Asset, Connection, DataQualityRule
from pyatlan.model.fluent_search import FluentSearch

client = AtlanClient()

# Example 1: Retrieve all data quality rules on a connection
search_request = ( # (1)
    FluentSearch()
    .select(include_archived=False) 
    .where_some(Connection.QUALIFIED_NAME.eq("default/databricks/1750768309"))
    .where_some(Asset.TYPE_NAME.eq("DataQualityRule"))
    .include_on_results(DataQualityRule.GUID)
    .include_on_results(DataQualityRule.QUALIFIED_NAME)
).to_request()

results = client.asset.search(search_request) # (2)
for result in results: # (3)
    print(f"Rule GUID: {result.guid}")
    print(f"Rule Qualified Name: {result.qualified_name}")

# Example 2: Retrieve all information of a specific data quality rule using its qualified name
search_request = (
    FluentSearch()
    .where(DataQualityRule.QUALIFIED_NAME.eq("default/databricks/1750768309/dq_poc/accuweather/_quality_monitoring_summary/rule/a481d03a-7fb9-48c1-a752-3aad4f6a98c1"))
    .include_on_results(DataQualityRule.GUID)
    .include_on_results(DataQualityRule.QUALIFIED_NAME)
    .include_on_results(DataQualityRule.DQ_RULE_BASE_COLUMN_QUALIFIED_NAME)
    .include_on_results(DataQualityRule.DQ_RULE_ALERT_PRIORITY)
    .include_on_results(DataQualityRule.DQ_RULE_DIMENSION)
).to_request()

result = client.asset.search(search_request)
search_result = result.current_page()[0] 

print(f"GUID: {search_result.guid}")
print(f"Qualified Name: {search_result.qualified_name}")
print(f"Column Qualified Name: {search_result.dq_rule_base_column_qualified_name}")
print(f"Alert Priority: {search_result.dq_rule_alert_priority}")
print(f"Dimension: {search_result.dq_rule_dimension}")

1. Create a Fluent Search request to retrieve data quality rules from a specific connection.
2. Execute the search request to retrieve the data quality rules.
3. Iterate through all matching data quality rules and print their details.

Kotlin

Coming soon

Raw REST API

Coming soon

Delete data quality rules

To delete data quality rules, you can use the standard asset deletion method.

Java

Coming soon

Python

Soft-delete data quality rules

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import DataQualityRule

client = AtlanClient()
response = client.asset.delete_by_guid(guid="b4113341-251b-4adc-81fb-2420501c30e6") # (1)
if deleted := response.assets_deleted(asset_type=DataQualityRule): # (2)
    term = deleted[0] # (3)

1. Use the asset.delete_by_guid() method to delete a data quality rule. Provide the GUID of the rule you want to delete.
2. The assets_deleted(asset_type=DataQualityRule) method returns a list of the assets of the given type that were deleted.
3. If an asset of the given type was deleted, then the deleted form of the asset is available.

Kotlin

Coming soon

Raw REST API

Coming soon

Set row scope filter column

To configure incremental data quality monitoring, use the set_dq_row_scope_filter_column method. This enables rules to scan only the most recent day's data instead of entire datasets, making monitoring faster and more efficient for batch-based ingestions.

Java

Coming soon

Python

Set row scope filter column

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import Table

client = AtlanClient()

response = client.asset.set_dq_row_scope_filter_column( # (1)
    asset_type=Table, # (2)
    asset_name="monitoring", # (3)
    asset_qualified_name="default/databricks/1750768309/dq/weather/monitoring", # (4)
    row_scope_filter_column_qualified_name="default/databricks/1750768309/dq/weather/monitoring/updated_at" # (5)
)

1. Use the set_dq_row_scope_filter_column method to configure incremental data quality monitoring.
2. Specify the asset type (for example, Table).
3. Provide the name of the asset as it appears in Atlan.
4. Provide the qualified name of the asset.
5. Provide the qualified name of the column to use for tracking row updates (typically a timestamp column).

Kotlin

Coming soon

Raw REST API

Coming soon

Schedule data quality rules

To add a schedule for data quality rules on an asset, you can use the add_dq_rule_schedule method. This method allows you to set up the schedule for data quality rule execution.

Java

Coming soon

Python

Add data quality rule schedule

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import Table

client = AtlanClient()

response = client.asset.add_dq_rule_schedule( # (1)
    asset_type=Table, # (2)
    asset_name="_quality_monitoring_summary", # (3)
    asset_qualified_name="default/databricks/1750768309/dq_poc/accuweather/_quality_monitoring_summary", # (4)
    schedule_crontab="41 20 * 1 *", # (5)
    schedule_time_zone="Europe/Paris" # (6)
)

1. Use the add_dq_rule_schedule method to add a schedule for data quality rules on an asset.
2. Specify the asset type (for example, Table).
3. Provide the name of the asset as it appears in Atlan.
4. Provide the qualified name of the asset (same as you would see in the Atlan UI).
5. Provide the cron schedule string following the standard cron format (for example, "41 20 * 1 *" means run at 20:41 only in January).
6. Provide the timezone string in the format used by Atlan UI (for example, "Europe/Paris", "Asia/Calcutta").

Kotlin

Coming soon

Raw REST API

POST /api/meta/entity/bulk

{
    "entities": [
        {
            "guid": "e971e35d-5d45-4d6c-a8e5-e2bc6a1e1c74", // (1)
            "typeName": "Table", // (2)
            "attributes": {
                "name": "_quality_monitoring_summary", // (3)
                "qualifiedName": "default/databricks/1750768309/dq_poc/accuweather/_quality_monitoring_summary", // (4)
                "assetDQScheduleType": "CRON", // (5)
                "assetDQScheduleCrontab": "41 20 * * 0,1,4-6", // (6)
                "assetDQScheduleTimeZone": "Asia/Calcutta" // (7)
            }
        }
    ]
}

1. The asset GUID to which the DQ rule schedule needs to be implemented.
2. The type of the asset (for example, "Table").
3. The name of the asset.
4. The qualified name of the asset.
5. Set to "CRON" for cron-based scheduling.
6. The cron schedule string (for example, "41 20 * * 0,1,4-6").
7. The timezone string (for example, "Asia/Calcutta").

Cron Schedule Format

The standard cron schedule format consists of five fields, separated by spaces:

• Minute (0-59): The minute of the hour when the command will run
• Hour (0-23): The hour of the day when the command will run (0 is midnight, 23 is 11 PM)
• Day of Month (1-31): The day of the month when the command will run
• Month (1-12): The month of the year when the command will run (1 is January, 12 is December)
• Day of Week (0-6): The day of the week when the command will run (0 is Sunday, 1 is Monday, and so on up to 6 for Saturday)