A dynamic mapping is a mapping that can accommodate changes to sources, targets, and transformation logic at run time. Use a dynamic mapping to manage frequent schema or metadata changes or to reuse the mapping logic for data sources with different schemas. Configure rules, parameters, and general transformation properties to create a dynamic mapping.
To create a dynamic streaming mapping, enable the refresh schema at the source or target, or enable the refresh schema of the mapping flow at the target. You can use the dynamic mapping for a streaming mapping by enabling refresh schema for Confluent Kafka sources or targets.
Dynamic Mapping Example
Every week, you receive customer data from different departments that you need to join and aggregate. The departments might periodically change the source schema to include additional columns for departmental analysis. To accommodate the changes to the data source, you create a dynamic mapping. You configure the Read transformation to get data object columns at read time. Create an input rule to include columns that you need and to exclude all other columns.
When your organization wants to manage frequent metadata changes in the streaming data sources, develop a dynamic streaming mapping that can get the metadata changes directly from the data sources at run time.
Effective in version 10.4.0, dynamic mapping support in Data Engineering Streaming is available for technical preview.
Technical preview functionality is supported for evaluation purposes but is unwarranted and is not production-ready. Informatica recommends that you use in non-production environments only. Informatica intends to include the preview functionality in an upcoming release for production use, but might choose not to in accordance with changing market or technical circumstances. For more information, contact Informatica Global Customer Support.