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  1. Abstract
  2. PowerExchange for Amazon Redshift
  3. PowerExchange for Amazon S3
  4. PowerExchange for Cassandra
  5. PowerExchange for Google BigQuery
  6. PowerExchange for Google Cloud Storage
  7. PowerExchange for Greenplum
  8. PowerExchange for HBase
  9. PowerExchange for HDFS
  10. PowerExchange for Hive
  11. PowerExchange for JDBC V2
  12. PowerExchange for JD Edwards EnterpriseOne
  13. PowerExchange for Kudu
  14. PowerExchange for LDAP
  15. PowerExchange for Microsoft Azure Blob Storage
  16. PowerExchange for Microsoft Azure Cosmos DB SQL API
  17. PowerExchange for Microsoft Azure Data Lake Storage Gen1
  18. PowerExchange for Microsoft Azure Data Lake Storage Gen2
  19. PowerExchange for Microsoft Azure SQL Data Warehouse
  20. PowerExchange for MongoDB
  21. PowerExchange for Netezza
  22. PowerExchange for OData
  23. PowerExchange for Salesforce
  24. PowerExchange for SAP NetWeaver
  25. PowerExchange for Snowflake
  26. PowerExchange for Teradata
  27. Informatica Global Customer Support

PowerExchange Adapters for Informatica Release Notes

PowerExchange Adapters for Informatica Release Notes

PowerExchange for Google BigQuery (10.5)

PowerExchange for Google BigQuery (10.5)

Known Issues

The following table describes known issues:
Issue
Description
OCON-26523
Google BigQuery mappings that run in the Amazon EMR 6.1 cluster fail with the following error:
NoClassDefFoundError: scala/Product$class
OCON-22940
If you use the
Create Target
option to create a Google BigQuery target and the source contains a column of Integer data type, the Data Integration Service fails to create a Google BigQuery target.
OCON-17956
The test connection is successful even when you specify incorrect credentials in the connection properties.

Third-Party Known Issues

The following table describes known issues:
Bug
Description
OCON-25252
When you use the Merge query to write data to a Google BigQuery target and import a Google BigQuery data object that contains more than 2000 columns, the mapping fails.
OCON-25412
When you run a Google BigQuery mapping on the Spark engine, the mapping fails when the following conditions are true:
  • You configure a Google BigQuery source and select
    Optimized
    as the
    Spark Mode
    .
  • You configure a Google BigQuery target and select
    Optimized
    as the
    Spark Mode
    .
  • You use any one of the following versions of the Hadoop distribution in the Hadoop environment:
    • Amazon EMR 5.29
    • Amazon EMR 5.26
    • Azure HDInsight 3.6
    • Cloudera CDH 5.16
    • Cloudera CDH 5.13
    • Hortonworks HDP 2.6
    • MapR
Google ticket reference number: 157261131
OCON-22676
When you run a mapping to read data of timestamp data type from a Google BigQuery source, incorrect values are written to the target for certain timestamp values.
Google ticket reference number: 142002729