Table of Contents

Search

  1. Abstract
  2. PowerExchange for Amazon Redshift
  3. PowerExchange for Amazon S3
  4. PowerExchange for Cassandra
  5. PowerExchange for Cassandra JDBC
  6. PowerExchange for DataSift
  7. PowerExchange for Facebook
  8. PowerExchange for Google Analytics
  9. PowerExchange for Google BigQuery
  10. PowerExchange for Google Cloud Spanner
  11. PowerExchange for Google Cloud Storage
  12. PowerExchange for Greenplum
  13. PowerExchange for HBase
  14. PowerExchange for HDFS (10.4.1)
  15. PowerExchange for HDFS (10.4.0)
  16. PowerExchange for Hive
  17. PowerExchange for JD Edwards EnterpriseOne
  18. PowerExchange for JDBC V2
  19. PowerExchange for LDAP
  20. PowerExchange for LinkedIn
  21. PowerExchange for MapR-DB
  22. PowerExchange for Microsoft Azure Blob Storage
  23. PowerExchange for Microsoft Azure Cosmos DB SQL API
  24. PowerExchange for Microsoft Azure Data Lake Storage Gen1
  25. PowerExchange for Microsoft Azure Data Lake Storage Gen2
  26. PowerExchange for Microsoft Azure SQL Data Warehouse
  27. PowerExchange for Microsoft Dynamics CRM
  28. PowerExchange for MongoDB
  29. PowerExchange for Netezza
  30. PowerExchange for OData
  31. PowerExchange for Salesforce
  32. PowerExchange for Salesforce Marketing Cloud
  33. PowerExchange for SAP NetWeaver
  34. PowerExchange for Snowflake
  35. PowerExchange for Tableau
  36. PowerExchange for Tableau V3
  37. PowerExchange for Teradata Parallel Transporter API
  38. PowerExchange for Twitter
  39. PowerExchange for Web Content-Kapow Katalyst
  40. Informatica Global Customer Support

PowerExchange Adapters for Informatica Release Notes

PowerExchange Adapters for Informatica Release Notes

PowerExchange for Hive Known Limitations (10.4.1)

PowerExchange for Hive Known Limitations (10.4.1)

The following table describes known limitations:
Bug
Description
BDM-33998
When you run a mapping on the Spark engine to write data to a Hive table with complex data types such as struct and the column name contains a reserved word, the mapping fails.
BDM-33990
When you run a mapping on the Spark engine to write data to a Hive table with complex data types such as struct and the column names have special characters, the mapping fails.
OCON-25634
When you alter a Hive table by adding a new column to a Hive partition table, the mapping fails with the following validation error:
"You cannot add column {columnname} as table {tablename} is partitioned"
OCON-25343
When you change the table metadata in the Developer Tool by modifying the existing data type in a Hive mapping that reads data from a Hive source and do not synchronize the Physical Data Object (PDO), the mapping fails with the following error:
FAILED: SemanticException [Error 10044]: Line 1:23 Cannot insert into target table because column number/types are different
Workaround: Synchronize the Physical Data Object and run the mapping again.
OCON-25211
When you run a mapping to read data from a Hive table with hierarchical (Htype) data type in columns and use the
sort
option to override the default SQL query, the mapping fails.
OCON-25180
When you synchronize a Hive object that contains complex datatypes in the Developer tool, the links between the hive objects in the mapping are not retained.