Both transformers could do this. The difference in this case could be in performance:
FeatureMerger needs all features to be read in the workspace. This option is better if most features are used in the join operation.
Joiner needs only one feature dataset in the workflow and will join to an external source. This option performs better if from the external source only a small part is joined. This way not the whole table has to be read.
Hope this helps supporting your choice.
Just dont forget to use the list setting for the 1:N relation
Both transformers could do this. The difference in this case could be in performance:
FeatureMerger needs all features to be read in the workspace. This option is better if most features are used in the join operation.
Joiner needs only one feature dataset in the workflow and will join to an external source. This option performs better if from the external source only a small part is joined. This way not the whole table has to be read.
Hope this helps supporting your choice.
Just did some looking and I believe the Joiner will be more efficient in this case because it stores the data it is joining to in a very compact efficient way. I'd start with Joiner anyway...
Just dont forget to use the list setting for the 1:N relation
Can you please tell me where to check the 1:N relation in FeatureMerger? Appreciate it!
Can you please tell me where to check the 1:N relation in FeatureMerger? Appreciate it!
In the setting to use duplicate suppliers. Then provide a list to store the duplicate suppliers.
Just dont forget to use the list setting for the 1:N relation
@erik_jan. ok, I have set Process Duplicate Suppliers to Yes. Should I use Merged or DuplicateSupplier port for output?