Table of Contents

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  1. Preface
  2. Introduction to Informatica Data Engineering Integration
  3. Mappings
  4. Mapping Optimization
  5. Sources
  6. Targets
  7. Transformations
  8. Python Transformation
  9. Data Preview
  10. Cluster Workflows
  11. Profiles
  12. Monitoring
  13. Hierarchical Data Processing
  14. Hierarchical Data Processing Configuration
  15. Hierarchical Data Processing with Schema Changes
  16. Intelligent Structure Models
  17. Blockchain
  18. Stateful Computing
  19. Appendix A: Connections Reference
  20. Appendix B: Data Type Reference
  21. Appendix C: Function Reference

Audits

Audits

To validate the consistency and accuracy of data processed in a mapping, you can create an audit for the mapping.
An audit is composed of rules and conditions. Use an audit rule to compute an aggregated value for a single column of data in the source or target. Use an audit condition to compare multiple rules or a rule and constant values. You can run audits in the native and Hadoop environments.
Running an audit on the Blaze engine is available for technical preview only.
Technical preview functionality is supported for evaluation purposes but is unwarranted and is not supported in production environments or any environment that you plan to push to production. Informatica intends to include the preview functionality in an upcoming release for production use, but might choose not to in accordance with changing market or technical circumstances. For more information, contact Informatica Global Customer Support.
You can configure audit rules for data sources and targets that use the following connectors:
Connector
Source
Target
Amazon S3
Supported.
Not supported.
HDFS
Supported.
Not supported.
Hive
Supported.
Supported.
JDBC V2
Supported.
Not supported.
Microsoft Azure Synapse
Supported.
Not supported.
Oracle
Supported.
Supported.
Snowflake
Supported.
Not supported.

Example

You are a data engineer for an e-commerce store. You have a data table with clothing sales information, including the sale price. You want to audit the data and verify that there are no outliers. You create a mapping that uses the clothing sales table as the source. You configure audit rules to calculate the maximum and minimum sales prices of the clothing from the Read transformation. After the mapping runs, you verify that the maximum and minimum sales prices are within the range you expect.