Create a new quality rule - Precisely Data Integrity Suite

Data Integrity Suite APIs

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Data_Integrity

Spatial_Analytics

Data_Enrichment

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Services

Spatial Analytics

Data Enrichment

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Data Integrity Suite APIs

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en-US

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pt_developer

copyrightfirst

2023

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2026

Creates a new quality rule with the provided details. Returns a status message.

HTML

POST https://hostname/v1/quality/rules

Request

Request body properties

Name	Type	Required	Description
name	string	Yes	Name of the quality rule. Must be unique within the account.
description	string	No	Description of the rule.
dimensionName	string	Yes	Specifies the data quality dimension the rule addresses and how results are grouped and reported. Valid values: `Accuracy`, `Completeness`, `Consistency`, `Timeliness`, `Uniqueness`, `Validity`.
ruleType	string	No	Specifies the type of rule evaluation logic. Supported values: `Expression`, `Sql`. If not provided, the rule is treated as Expression by default.
passCondition	object	Yes	Defines the logic that determines whether a record passes the rule. This is the core rule logic evaluated by the rule engine.
passCondition.type	string	Yes	Specifies the condition type. Interpreted based on `ruleType` (Expression or SQL).
passCondition.config	string	Yes	Expression or SQL logic evaluated by the rule. Syntax depends on `ruleType`.
filterCondition	object	No	Optional pre-filter applied before rule evaluation. Records that do not match this condition are excluded from evaluation.
filterCondition.type	string	Conditional	Specifies the filter condition type. Required if `filterCondition` is provided.
filterCondition.config	string	Conditional	Expression or SQL used to filter records before apply the `passCondition`.
failedCondition	object	No	Optional condition that explicitly defines failed records. Applicable for SQL rules and uses the same {type, config} structure as passCondition and filterCondition.
failedCondition.type	string	Conditional	Specifies the failed‑condition type. Required if `failedCondition` is provided. Interpreted based on `ruleType`.
failedCondition.config	string	Conditional	Expression or SQL identifying failed records. Evaluated based on `ruleType`.
isFailedCondition	boolean	No	Indicates whether the failedCondition should be evaluated. For SQL rules, when `failedCondition` is provided, this value must be set to `true`.
captureFailedRecords	boolean	No	When `true`, failed records are captured for inspection as part of rule results.
enableAutoRun	boolean	No	Controls whether the rule is executed automatically based on system conditions. When `true`, the rule is automatically triggered based on system conditions.
targetingType	string	Yes	Specifies the scope at which the rule is applied. Determines whether the rule applies at the `Field`, `Dataset`, or `FieldByCondition` level.
targetAssetsFilter	array<string>	Yes	Filter expressions used to select target assets for rule evaluation. Controls which assets the rule applies to.
mappings	array<object>	Yes (if targetingType is `Field` or `Dataset`)	Array of mapping objects that define how dataset columns are mapped for rule evaluation. Required when `targetingType` is `Field` or `Dataset`; not required for `FieldByCondition`.
mappings[].datasetId	string	Conditional	Identifier of the dataset used by the rule. Required when mappings is provided.
mappings[].columnMapping	array<object>	Conditional	Defines column‑level mappings used to bind rule logic to dataset columns during rule evaluation. Required when mappings is provided.
mappings[].columnMapping[].columnName	string	Conditional	Name of the source dataset column referenced by the rule.
mappings[].columnMapping[].dataType	string	Conditional	Data type of the column, used by the rule engine during evaluation.
mappings[].columnMapping[].alias	string	No	Alias for the column used in expressions to simplify logic.
mappings[].columnMapping[].targetField	string	No	Target field name used internally by the rule engine for rule processing.

Examples of valid rule conditions

The following examples illustrate valid rule conditions using Expression and SQL syntax. They demonstrate function usage and show how rule behavior defaults to Expression when ruleType is not specified.

Example 1 - Default Expression rule

If ruleType is not specified, the rule is treated as an Expression rule by default.

{
  "name": "firstname_not_null",
  "dimensionName": "Completeness",
  "targetingType": "Field",
  "targetAssetsFilter": [
    "properties.CatalogType:eq(Field)"
  ],
  "passCondition": {
    "type": "Expression",
    "config": "FIRSTNAME IS NOT NULL"
   }
}

Example 2 - Explicit Expression rule with function usage

{
  "name": "recent_order_check",
  "ruleType": "Expression",
  "dimensionName": "Timeliness",
  "targetingType": "Dataset",
  "targetAssetsFilter": [
    "properties.CatalogType:eq(Dataset)"
  ],
  "passCondition": {
    "type": "Expression",
    "config": "ORDER_DATE >= TO_DATE('2024-01-01')"
  }
}

Example 3 - SQL rule with date-time function

{
  "name": "active_records_sql_rule",
  "ruleType": "Sql",
  "dimensionName": "Accuracy",
  "targetingType": "Dataset",
  "targetAssetsFilter": [
    "properties.CatalogType:eq(Dataset)"
  ],
  "passCondition": {
    "type": "Sql",
    "config": "SELECT * FROM input WHERE CREATED_TS >= TO_DATETIME('2024-01-01 00:00:00')"
  }
}

Request body property operators

Tables in this section describe operators to be used in specified request body property values.

Table 1. Operators for targetAssetsFilter property setting.
For the targetAssetsFilter property setting, choose from operators in this table.

The `eq` and `ne` operators evaluate single values while the `in` and `ni` operators evaluate lists.

`in`: value matches any value in a list or set

`ni`: value does not match any value in a list or set

`eq`: equal to

`ne`: not equals to

`eq("is populated")`: contains any value (not null)

`eq("is not populated")`: contains no value (is null)
`targetingType` value	Catalog type	Filter	Operators
`Field`	Field		For a single field, use the `eq` operator. For multiple fields, use the `in` operator
`Dataset`	Dataset		For a single dataset, use the `eq` operator. For multiple datasets, use the `in` operator
`FilterByCondition`	Datasource	filter = name	`in` `ni`
	Datasource	filter = name	eq("is populated") eq("is not populated")
	Dataset	filter = id	`in` `ni`
	Dataset	filter = value	`eq("is populated")` `eq("is not populated")`
	Semantic type	filter = qualifier	`eq` `ne`
	Semantic type	filter = value	`eq("is populated")` `eq("is not populated")`

Table 2. Operators for passCondition and filterCondition properties.
For passCondition and filterCondition use these operators while formulating a condition into the Natural Language.
For this operation	Use this operator
`is`	`=`
`is not`	`!=`
`is populated`	`IS NOT NULL`
`is not populated`	`IS NULL`
`Value in the array values`	`IN`
`is greater than`	`>=`
`is greater than or equal to`	`>=`
`is less than`	`<=`
`is less than or equals to`	`<=`
`Date`	`TO_DATE('YYYY-MM-DD')`, for example `TO_DATE('1990-12-07')`
`Time`	`TO_TIME('HH:MI:SS')`, for example `TO_TIME('11:25:31')`
`Date-time`	`TO_DATETIME('YYYY-MM-DD HH:MI:SS')`, for example `TO_DATETIME('1990-12-07 11:25:31')`

Request example

Example request body for this method is shown below. You can create rules in three ways by setting the targetingType property to Field, Dataset, or FieldByCondition.

Examples

{
  "name": "rule_name",
  "description": "rule_description",
  "dimensionName": "dimension_name",
  "targetAssetsFilter": [
    "properties.CatalogType:eq(Field)&filter=id:eq(asset_id)"
  ],
  "mappings": [
    {
      "datasetId": "dataset_id",
      "columnMapping": [
        {
          "columnName": "column_name",
          "dataType": "string",
          "alias": "column_alias",
          "targetField": "column_name"
        }
      ]
    }
  ],
  "captureFailedRecords": true,
  "enableAutoRun": true,
  "targetingType": "Field",
  "passCondition": {
    "type": "script",
    "config": "condition_expression"
  },
  "filterCondition": {
    "type": "script",
    "config": "filter_expression"
  }
}

{
  "id": "rule_id",
  "name": "rule_name",
  "description": "rule_description",
  "dimensionName": "dimension_name",
  "passCondition": {
    "type": "script",
    "config": "condition_expression"
  },
  "captureFailedRecords": true,
  "enableAutoRun": true,
  "targetingType": "Dataset",
  "targetAssetsFilter": [
    "properties.CatalogType:eq(Dataset)&filter=id:eq(asset_id)"
  ],
  "mappings": [
    {
      "datasetId": "dataset_id",
      "columnMapping": [
        {
          "columnName": "column_name",
          "dataType": "string",
          "alias": "column_alias"
        }
      ]
    }
  ]
}

{
    "name": "rule_name",
    "description": "rule_description",
    "dimensionName": "dimension_name",
    "targetAssetsFilter": [
        "properties.CatalogType:eq(Datasource type)&filter=name:in(dataSource_type)",
        "properties.CatalogType:eq(Datasource)&filter=displayValue:in(dataSource_name)",
        "properties.CatalogType:eq(Dataset)&filter=displayValue:in(dataset_name)",
        "properties.CatalogType:eq(Semantic type)&filter=qualifier:eq(semantic_name)"
    ],
    "captureFailedRecords": true,
    "enableAutoRun": true,
    "targetingType": "FieldByCondition",
    "passCondition": {
        "type": "script",
        "config": "condition_expression"
    },
    "filterCondition": {
        "type": "script",
        "config": "filter_expression"
    }
}

{
  "name": "conditional_field_rule",
  "description": "Apply rule to fields selected dynamically by conditions",
  "dimensionName": "Accuracy",
  "targetAssetsFilter": [
    "properties.CatalogType:eq(Datasource)&filter=displayValue:in(dataSource_name)",
    "properties.CatalogType:eq(Dataset)&filter=displayValue:in(dataset_name)"
  ],
  "captureFailedRecords": true,
  "enableAutoRun": true,
  "targetingType": "FieldByCondition",
  "passCondition": {
    "type": "Expression",
    "config": "VALUE IS NOT NULL"
  },
  "filterCondition": {
    "type": "Expression",
    "config": "STATUS = 'ACTIVE'"
  }
}

Response

The successful response returns the unique identifier for the new quality rule.

Response example

Example status code responses for this method are shown here.

Examples

Rule created successfully.

{
"id":"693eed89aa5bb52e34cc61dd"
}

{
  "type": "about:blank",
  "title": "Bad Request",
  "status": 400,
  "detail": "Invalid rule request - Rule must have a name present",
  "instance": "/v1/quality/rules",
  "errorCode": "DIS-QUALITY-RULES-USR-0006"
}

{
  "type": "about:blank",
  "title": "Internal Server Error",
  "status": 500,
  "detail": "An unexpected error occurred on the server",
  "instance": "/v1/quality/rules",
  "errorCode": "DIS-QUALITY-RULES-SVC-0001"
}