Developer Mapping Guide

10.4.0
- 10.5.6
- 10.5.4
- 10.5
- 10.4.1
- 10.2.2
- 10.2.1

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Dynamic Targets

A dynamic target is a target that can change at run time.

When you run a mapping, a dynamic target can get metadata changes from physical data targets, including relational tables, flat files, Amazon Redshift, Amazon S3, Microsoft Azure Data Lake Store, Microsoft Azure Blob Storage, HDFS, HBase and customized data objects. It can also generate columns based on the upstream column definitions.

You can configure dynamic run-time functionality for a target in the following ways:

Get columns from the data source.: When you expect small changes to the target, you can configure the Write transformation to get relational object columns at run time. When you configure the Write transformation to get metadata from targets, you can configure the Write transformation to update dynamically and remain synchronized with target objects.
Define target columns based on the mapping flow.: When you define columns based on the mapping flow, target columns are determined by upstream transformations.
Define target columns based on the data object.: When you define columns based on the data object, target columns are determined by the associated data object.
Define a target schema strategy.: You can define a target schema strategy to retain the target schema, or to create or replace target tables at run time. You can also specify a parameter value for the target schema strategy in the Write transformation.; When you configure a target schema strategy in the Write transformation to create or replace the target at run time, the Data Integration Service creates the target based on the data object or on the mapping flow. You can also define a DDL query to create the target based on the query.
Assign a parameter to determine the resource, table owner, or directory of a relational data object.: When the relational targets are similar, you can assign a parameter to get the resource, connection, and table owner properties.
Assign a parameter to determine the data object to use for a file or relational target.: You can create a customized data object as a Write transformation, and specify a parameter value as the target for the transformation. When you change the value of the parameter, the target changes for all objects that use the parameter.

The following table shows where you can configure the dynamic run-time functionality of a target:

Dynamic Run-time Target Functionality	Configuration
Get columns from the data source.	Configure the Data Object tab on the Write transformation for the following target types: Relational Amazon Redshift Amazon S3 Microsoft Azure Blob Storage Microsoft Azure Data Lake Store HBase HDFS Snowflake
Define target columns based on the data object or the mapping flow.	Configure the Ports tab on the Write transformation for the following target types: Flat file Relational Amazon Redshift Amazon S3 Microsoft Azure Blob Storage Microsoft Azure Data Lake Store HBase HDFS Snowflake
Select a target schema strategy.	Configure the Advanced tab on the physical data object for the following target types: Relational Amazon Redshift Snowflake
Define a DDL query to create the target table at run time.	Configure the Advanced tab on the physical data object for the following target type: Relational
Assign a parameter to determine the connection, owner, or resource.	Configure the Run-Time tab on the Write transformation for the following target types: Relational Amazon Redshift Amazon S3 Snowflake
Assign a parameter to determine the data object.	Configure the Data Object tab on the Write transformation for the following target types: Flat file Relational Amazon Redshift Amazon S3 Snowflake