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

Rules and Guidelines for Data Types

Rules and Guidelines for Data Types

Consider the following rules and guidelines for data types:
  • Avro data types support:
    • Date, Decimal, and Timestamp data types are applicable when you run a mapping in the native environment or on the Spark engine in Cloudera CDH 6.1 distribution only.
    • Time data type is applicable when you run a mapping in the native environment in Cloudera CDH 6.1 distribution only.
  • Parquet data types support:
    • Date, Time, and Timestamp data types till micros are applicable when you run a mapping in the native environment and Blaze engine in the CDH 6.1, HDP 3.1, and HDI 4.0 distributions.
    • Date and Timestamp data types till micros are applicable when you run a mapping on the Spark engine in the CDH 6.1, HDP 3.1, and HDI 4.0 distributions.
    • Date,Time_Millis and Timestamp_Millis data typesare applicable when you run a mapping in the native environment or Blaze engine in the EMR 5.23, MapR6.1, and HDP 2.6 distributions.
    • Date and Timestamp_Millis data types are applicable when you run a mapping on the Spark engine in the EMR 5.23, MapR6.1, and HDP 2.6 distributions.
    • Decimal data types are applicable when you run a mapping in the native environment and Blaze engine in Cloudera CDH 6.1, CDH 6.3, HDP 2.6, HDP 3.1, EMR 5.20, EMR 5.23, MapR 6.1, Dataproc 1.4 and HDI 4.0 distributions.
    • Date, Time, Timestamp, and Decimal data types are applicable when you run a mapping on the Databricks Spark engine.
  • When you run a mapping in the native environment and use Time data type in the source, the Data Integration Service writes incorrect date value to the target.
    For example, Time data type used in the source:
    1980-01-09 06:56:01.365235000
    Incorrect Date value is generated in the target:
    1899-12-31 06:56:01.365235000
  • When you run a mapping in the native environment and use Date data type in the source, the Data Integration Service writes incorrect time value to the target.
    For example, Date data type used in the source:
    1980-01-09 00:00:00
    Incorrect Time value generated in the target:
    1980-01-09 05:30:00
  • To run a mapping that reads and writes Date, Time, Timestamp, and Decimal data types, update the
    -DINFA_HADOOP_DIST_DIR
    option to the
    developerCore.ini
    file. The
    developerCore.ini
    file is located in the following directory:
    <Client installation directory>\clients\DeveloperClient\
    Add the following path to the
    developerCore.ini
    file:
    -DINFA_HADOOP_DIST_DIR=hadoop\CDH_6.1
    Update
    developerCore.ini
    for all file-based PowerExchange adapters except PowerExchange for HDFS.
  • To use precision up to 38 digits for Decimal data type in the native environment, set the
    EnableSDKDecimal38
    custom property to
    true
    for the Data Integration Service. The
    EnableSDKDecimal38
    custom property is applicable to all file-based PowerExchange adapters except PowerExchange for HDFS.


Updated September 28, 2020