Validate quality pipeline

Data Integrity Suite
Product
Spatial_Analytics
Data_Integration
Data_Enrichment
Data_Governance
Precisely_Data_Integrity_Suite
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Data_Observability
Data_Quality
dis_core_foundation
Services
Spatial Analytics
Data Integration
Data Enrichment
Data Governance
Geo Addressing
Data Observability
Data Quality
Core Foundation
ft:title
Data Integrity Suite
ft:locale
en-US
PublicationType
pt_product_guide
copyrightfirst
2000
copyrightlast
2025

Validate your pipeline to ensure it runs successfully without any issues. The output will not be generated if the pipeline fails to execute. The errors can be present in either the individual transformation steps or the Quality pipeline.

Quality validation overview

As you add, remove, or edit steps in a pipeline, Data Quality validates changes to the pipeline and checks for errors. A validation issue is represented on a step by a warning symbol , an error symbol , or a data error symbol . An error prevents execution of a pipeline. A warning does not prevent execution of a pipeline, but it may create unpredictable or unexpected outcomes.

Example:

Consider this pipeline with three steps.
  1. The Rename Column step renames a column from "Bank" to "FinancialInstitution".
  2. The Search and Replace step searches and replaces content in the renamed "FinancialInstitution" column.
  3. The Case Field step changes field content to title case in the renamed "FinancialInstitution" column and in a "City" column.
If you delete the first step, Data Quality would validate that change. Because the second and third steps refer to the renamed columns in the first step, the system would present an error and warning.

  • An error shows for the Search and Replace step because there will no longer be a "FinancialInstitution" renamed column.

  • A warning shows for the Case Field step because although there is no longer a "FinancialInstitution" column the step will still be able to change case in the unaffected "City" column. If the Case Field step acted only on the deleted "FinancialInstitution" column, then it too would show an error instead of the warning.

The Data Quality pipeline does not prevent you from making changes that create errors. You can choose to make a desired change that creates an error and later fix the problems caused the error. Remember, however, you can execute a pipeline with a warning, but you cannot execute a pipeline with an error.
In this example, you could:
  • Resolve the error by reconfiguring the Search and Replace step to search the previously renamed "Bank" column.
  • Resolve the warning by removing the reference to the "FinancialInstitution" column or by reconfiguring the Case Field step to change case in the previously renamed "Bank" column.

What causes data errors

Issues such as verification failures, invalid types or formats, or inability to parse data can cause data errors. A data error symbol displays on any step that generates a data error. When you click a step with a data error, red bars mark the columns and rows in the table that contain the errors. Fields that contain errors are highlighted. To view the cause of a data error, you can hover the pointer over a highlighted cell. Use error and warning symbols on steps in a pipeline to guide you through changes necessary to create an error-free pipeline.

Changes you make may create new errors or warnings in subsequent steps. When that happens, proceed iteratively through successive edits and validations to eliminate all errors and warnings from a pipeline. You may choose to add additional transformation steps to fix data errors.