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  • Common Content for Data Engineering 10.2.1
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Inhaltsverzeichnis

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  1. Preface
  2. Einführung in Umwandlungen
  3. Umwandlungsports
  4. Umwandlungs-Caches
  5. Adressvalidator-Umwandlung
  6. Aggregator-Umwandlung
  7. Assoziations-Umwandlung
  8. Ausnahmeumwandlung bei fehlerhaftem Datensatz
  9. Fallumwandlerumwandlung
  10. Klassifizierumwandlung
  11. Vergleichsumwandlung
  12. Konsolidierungsumwandlung
  13. Datenmaskierungsumwandlung
  14. Datenprozessor-Umwandlung
  15. Entscheidungsumwandlung
  16. Ausnahmeumwandlung bei dupliziertem Datensatz
  17. Ausdrucksumwandlung
  18. Filterumwandlung
  19. Umwandlung von hierarchisch in relational
  20. Java-Umwandlung
  21. Java-Umwandlungs-API-Referenz
  22. Java-Ausdrücke
  23. Joiner-Umwandlung
  24. Schlüsselgeneratorumwandlung
  25. Labeler-Umwandlung
  26. Lookup-Umwandlung
  27. Lookup-Caches
  28. Dynamischer Lookup-Cache
  29. Match-Umwandlung
  30. Match-Umwandlungen in der Feldanalyse
  31. Match-Umwandlungen in der Identitätsanalyse
  32. Merge-Umwandlung
  33. Normalizer-Umwandlung
  34. Parser-Umwandlung
  35. Python-Umwandlung
  36. Rang-Umwandlung
  37. Leseumwandlung
  38. Relational-in-hierarchisch-Umwandlung
  39. REST-Webdienst-Verbraucher-Umwandlung
  40. Router-Umwandlung
  41. Sequenzgeneratorumwandlung
  42. Sortierer-Umwandlung
  43. SQL-Umwandlung
  44. Standardisierungs-Umwandlung
  45. Union-Umwandlung
  46. Update-Strategie-Umwandlung
  47. Web-Dienst-Verbraucher-Umwandlung
  48. Parsen von Webdienst-SOAP-Meldungen
  49. Generieren von Webdienst-SOAP-Meldungen
  50. Umwandlung für gewichteten Durchschnitt
  51. Schreibumwandlung
  52. Umwandlungs-Delimiter

Developer Transformation Guide

Developer Transformation Guide

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Java Transformation Suppport on the Hive Engine

Java Transformation Suppport on the Hive Engine

You can enable the Stateless advanced property when you run mappings in a Hadoop environment.
The Java code in the transformation cannot write output to standard output when you push transformation logic to Hadoop. The Java code can write output to standard error which appears in the log files.
Some processing rules for the Hive engine differ from the processing rules for the Data Integration Service.

Partitioning

You can optimize the transformation for faster processing when you enable an input port as a partition key and sort key. The data is partitioned across the reducer tasks and the output is partially sorted.
The following restrictions apply to the Transformation Scope property:
  • The value Transaction for transformation scope is not valid.
  • If transformation scope is set to Row, a Java transformation is run by mapper script.
  • If you enable an input port for partition Key, the transformation scope is set to All Input. When the transformation scope is set to All Input, a Java transformation is run by the reducer script and you must set at least one input field as a group-by field for the reducer key.

Using External .jar Files

To use external .jar files in a Java transformation, perform the following steps:
  1. Copy external .jar files to the Informatica installation directory in the Data Integration Service machine at the following location:
    <Informatic installation directory>/services/shared/jars
    . Then recycle the Data Integration Service.
  2. On the machine that hosts the Developer tool where you develop and run the mapping that contains the Java transformation:
    1. Copy external .jar files to a directory on the local machine.
    2. Edit the Java transformation to include an import statement pointing to the local .jar files.
    3. Update the classpath in the Java transformation.
    4. Compile the transformation.