Configure row filters and conditions - Precisely Data Integrity Suite

Data Integrity Suite
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Spatial_Analytics
Data_Integration
Data_Enrichment
Data_Governance
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Data_Observability
Data_Quality
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Services
Spatial Analytics
Data Integration
Data Enrichment
Data Governance
Geo Addressing
Data Observability
Data Quality
Core Foundation
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Data Integrity Suite
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en-US
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pt_product_guide
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2000
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2026

Row filters and conditions let you apply rules selectively to specific rows and define how results are evaluated. You can configure filters using basic or advanced conditions or SQL queries, and for SQL rules, you can define a fail condition to mark records as failures for use in a quality pipeline.

Conditional rules with data filters improve processing accuracy and efficiency. This feature lets you apply rules to specific datasets and fields for more relevant insights.

When you create a rule, you can define a row filter that works like a SQL WHERE clause to specify which rows the rule evaluates. For SQL rules, you can also define a fail condition to mark evaluated records as failures. Records that meet the fail condition can then be used as a source in a quality pipeline.

Configure row filters using basic or advanced conditions

  1. On the Create Rule page, select Add Evaluated Rows Filter.
    What's changed: In the new user experience, Add Evaluated Rows Filter appears as Add Row Filters. For more information on the new experience, see About the new user experience.
  2. Choose to evaluate the rows using Basic or Advanced conditions.
    • Basic conditions are suitable for simple evaluation scenarios. The initial value field acts as an alias for all target fields. Any condition configured in the Basic tab is automatically converted to an expression in the Advanced tab. Select one of the following combinations:
      • <attribute><operator><enter value><value> — type a value that the condition must match.
      • <attribute><operator><select field><field> — select a field of the same data type to compare against. This is supported for all available data types.
    • Advanced conditions are suitable for complex evaluation scenarios. Configure an expression using numerical, string, and logical operators.
      Tip: You can auto-generate the expression for advanced conditions using the AI Assist option. For more information, refer to Use AI to generate and validate rule expressions.
  3. Click Add filter to add more conditions, then select Match All (logical AND) or Match Any (logical OR) to define how multiple conditions are combined.
  4. After the rows are filtered, apply pass conditions for the filtered data assets.
    Note: All fields added from the evaluated row filter must be mapped to the reference dataset or reference field.
  5. Save the rule and run it.

Configure row filters using a SQL query

  1. On the Create Rule page, select Add Evaluated Rows Filter.
    What's changed: In the new user experience, Add Evaluated Rows Filter appears as Add Row Filters. For more information on the new experience, see About the new user experience.
    Note: The SQL query option is only available when target assets are selected through the SQL option.
  2. Enter your SQL query in the text box. You can use the available SQL functions and correlate data across multiple tables.
    Note: Write a SQL query that returns a valid set of records. CRUD operations are not supported; your query must not include DROP, DELETE, or UPDATE statements.
  3. After the query is entered, the specified conditions are evaluated, and the corresponding rows are filtered.
  4. After successful rule evaluation, review the evaluated count, which shows the total number of records filtered by the row filter conditions.

Configure a fail condition for SQL rules

A fail condition in SQL rules defines when a logical test or constraint determines that a rule has failed. Records evaluated against this rule are then marked as failed records. When you define fail conditions, if a Data Quality pipeline uses the SQL rule as a source, the pipeline processes only the records that meet the defined fail condition, along with the fields you selected in the SQL rule.
  • Choose Fail condition to process failed records.
  • Make sure to select the fields you want available in the Data Quality pipeline; the pipeline can only process fields selected in this rule.
  1. On the Create Rule page, click Fail condition.

    Select the fields you want available in the Data Quality pipeline. The pipeline can process only the fields selected in this rule.

  2. Enter your SQL query in the Fail condition text box. You can use the available SQL functions and correlate data across multiple tables.
  3. After the query is entered, the conditions are evaluated and the rule runs against the defined fail conditions.
  4. After rule evaluation completes, review the failed count, which shows the total number of records that met the defined fail condition.
After a rule with a fail condition is evaluated, you can use the records it processed as a source in a quality pipeline.