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

Data Processor Mappings

Data Processor Mappings

The Data Processor transformation processes unstructured and semi-structured file formats in a mapping. It converts source data to flat CSV records that MapReduce applications can process.
You can configure the Data Processor transformation to process messaging formats, relational data, HTML pages, XML, JSON, AVRO, Parquet, Cobol, Microsoft Excel, Microsoft Word, and PDF documents. You can also configure it to transform structured formats such as ACORD, HIPAA, HL7, EDI-X12, EDIFACT, and SWIFT.
For example, an application produces hundreds of data files per second and writes the files to a directory. You can create a mapping that extracts the files from the directory, passes them to a Data Processor transformation, and writes the data to a target.


Updated September 28, 2020