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

Column Profiles for Sqoop Data Sources

Column Profiles for Sqoop Data Sources

You can run a column profile on data objects that use Sqoop. After you choose Hadoop as a validation environment, you can select the Blaze engine or Spark engine on the Hadoop connection to run the column profiles.
When you run a column profile on a logical data object or customized data object, you can configure the num-mappers argument to achieve parallelism and optimize performance. You must also configure the split-by argument to specify the column based on which Sqoop must split the work units.
Use the following syntax:
--split-by <column_name>
If the primary key does not have an even distribution of values between the minimum and maximum range, you can configure the split-by argument to specify another column that has a balanced distribution of data to split the work units.
If you do not define the split-by column, Sqoop splits work units based on the following criteria:
  • If the data object contains a single primary key, Sqoop uses the primary key as the split-by column.
  • If the data object contains a composite primary key, Sqoop defaults to the behavior of handling composite primary keys without the split-by argument. See the Sqoop documentation for more information.
  • If a data object contains two tables with an identical column, you must define the split-by column with a table-qualified name. For example, if the table name is CUSTOMER and the column name is FULL_NAME, define the split-by column as follows:
    --split-by CUSTOMER.FULL_NAME
  • If the data object does not contain a primary key, the value of the m argument and num-mappers argument default to 1.
When you use Cloudera Connector Powered by Teradata or Hortonworks Connector for Teradata and the Teradata table does not contain a primary key, the split-by argument is required.


Updated September 28, 2020