Using the data migration tool is fairly simple; however, an
understanding of different business entities and their data model is highly
required to successfully migrate the data.
Add the entities into the processing group.
With this I close the post and hope it will give you a kick
start leveraging other really powerful features of DMF.
In this post, I will take a sample scenario to walk through
the configuration and setup of data migration framework. We will consider
couple of entities to migrate the data. Let’s assume that we need to migrate customer
master data from a source system to AX 2012. Follow these steps to achieve the
objective:
Navigate to Data Migration Framework module in AX 2012
application workspace. Open Setup>Target entities form. 
The target entities out of the box available are listed in
this form. You can select an entity and modify its staging to target mapping if
you would like to (in case you have customizations in AX). Here, we are going
with what is available out of the box. You can also validate the mapping after
modifying it, view the target entity hierarchy, and target fields for any
selected entity. You can also create a new record for a custom target entity.
Now we have to define the data format for our source data. Open
Source data formats from Setup area and create a new record as shown below:
I have selected File format as Delimited with first row as
header and values are separated with comma. Also marked the source file to be Unicode.
It is often the case that primary entity might have multiple sub records
associated with it. For example, a customer might have two addresses. In such
situation, you can use the Role Separator to distinguish these multiple
records.
Now the next step is to create processing group (Common>Processing
group) for customer data migration. By definition, processing group is a logical
grouping of entities that need to be imported in sequential order.
At this point, let’s use the generate source file wizard to
generate the source customer file for import. You can also create your own
source file as per the format specified in Source data formats.
Click Next. On the screen below, you have the option to only
use the fields present in source for customer import or include other fields.
Click Generate sample file and the wizard will provide you a
file as shown below. Save the file in a folder as later we will need to refer
to that location.
Once the file is generated and saved, put some data in it
and provide its location in the Sample file path field.
Our next step is to generate the source to staging mapping.
Note that you get the infolog “Mapping has completed successfully”. At this
point, you can also modify the source to staging mapping. In our case, we don’t
have to.
Target mapping with corresponding staging table can also be
viewed. You also have the option to run the business logic or validations in
entity’s insert update or validateWrite methods at the time of import. Data
preview for staging table can also be analyzed in the preview pane. This is how
the data will look like in the staging table.
Next step is to push the data from source file to staging
table.
A dialog will appear with auto generated job Id. Click OK
and Staging data execution screen will appear.
Click Run. You can also run this job on AOS if you would
like to. You can also preview the staging data here. After the job is executed,
an infolog will appear mentioning that a record has been successfully created
in the staging table.
Once the data is in staging table, it just the matter of
pushing it to target (AX) entity’s table.
A dialog will appear asking for the job that was executed
previously to push the data from source to staging. Select ‘Customer-6’ in this
case.
Click OK. Target data processing screen will appear.
Click Run. After the job is executed, an infolog will appear
mentioning the record is created in the target entity.
You can cross verify in ‘CustTable’ and corresponding
staging table ‘DMFCustomerEntity’ for customer entity.
The framework gives really cool feature of exception
logging. You can see any errors that might have occurred during the
source-staging or staging-target job execution.
