Table of Contents


  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings in the Hadoop Environment
  4. Mapping Sources in the Hadoop Environment
  5. Mapping Targets in the Hadoop Environment
  6. Mapping Transformations in the Hadoop Environment
  7. Processing Hierarchical Data on the Spark Engine
  8. Configuring Transformations to Process Hierarchical Data
  9. Processing Unstructured and Semi-structured Data with an Intelligent Structure Model
  10. Stateful Computing on the Spark Engine
  11. Monitoring Mappings in the Hadoop Environment
  12. Mappings in the Native Environment
  13. Profiles
  14. Native Environment Optimization
  15. Cluster Workflows
  16. Connections
  17. Data Type Reference
  18. Function Reference
  19. Parameter Reference

Hive Targets

Hive Targets

A mapping that is running in the Hadoop environment can write to a Hive target.
A Hive target can be an internal table or an external table. Internal Hive tables are managed by Hive and are also known as managed tables. External Hive tables are managed by an external source such as HDFS, Amazon S3, Azure Blob, WASB, or ADLS.
Consider the following restrictions when you configure a Hive target in a mapping that runs in the Hadoop environment:
  • A mapping fails to run when you use Unicode characters in a Hive target definition.
  • When you set up a dynamic target for a partitioned Hive table, the value used for the partition is the final column in the table. If the table has a dynamic partition column, the final column of the table is the dynamic partition column. To use a different column for the partition, move it to the last column of the table. If the table has multiple partition columns, the dynamic partition values are selected from the last columns of the upstream transformation. You can use an Expression transformation to reorder the columns if necessary.
When a mapping creates or replaces a Hive table, the type of table that the mapping creates depends on the run-time engine that you use to run the mapping.
The following table shows the table type for each run-time engine:
Run-Time Engine
Resulting Table Type
You can design a mapping to truncate an internal or external Hive table that is bucketed and partitioned.
In a mapping that runs on the Spark engine or the Blaze engine, you can create a custom DDL query that creates or replaces a Hive table at run time. However, with the Blaze engine, you cannot use a backtick (`) character in the DDL query. The backtick character is required in HiveQL when you include special characters or keywords in a query.
The Spark engine can write to bucketed Hive targets. Bucketing and partitioning of Hive tables can improve performance by reducing data shuffling and sorting.

Updated October 23, 2019