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Output Container

Output Container is the storage area for the transformed data (if any transformation rules are applied) which are considered for the deployment. In order to see any data in the Output Container, the Filters have to be set for a specific Migration Objects and Target Table. There are two different Data Status fields for the data in the Output Container and one more Deployment Status.The Record Status shows the evolvement of the Record from, New, Modified, Existing, Approved or Deleted. The Data Status handles the quality of the Data in the Record like, Not Validated, Error, Validated and Ready to be Deployed. The Deploy Status, reflects if the Record has been Deployed to any of the Target Environments. These Status fields can be used to select for Rerun of Validation Tests.

This container has a generic table structure similar to Input Container with 12 columns to store key field values, 400 columns to store non-key field values and all those 412 columns has adjascent columns to store the messages that are generated during validations

Meta Data Validation

If any Errors exists during meta data validation, the Data Status is set to Error. The columns containing Errors, will have a description in its related Messages column next to it, showing exactly what Error needs to be fixed. To handle Meta Data Errors in the Output Container, there needs to be some kind of Transformation Rule applied, and a Rerun from Input Container to update the value. Normally, these errors should have been identified already in the Input Container and Transformed already.

Basic Data Validation

Basic Data Validation is performed to control that table values are matched against the underlying Sub table's (references) Approved values. Validation process searches for values in both Basic Data as well as in Master or Transactional Data Tables. Basic Data is stored in the Basic Data Container, while Master and Transactional Data is stored in the Output Container. The record status, in these tables, needs to be at Approved Level to be considered in the validation process.

If Basic Data Error occurs there are two ways to handle these:

  • Transform the field value with a Transformation Rule and Rerun the transfer from Input Container, or
  • Extract the missing Value from the Error field into the Basic Data Container. Then Validate and Approve these new Values in the Basic Data Container. Then Validate the data in the Output Container again.

Basic Data Extraction

This Scenario is used to Extract missing Basic Data from the Output Container, to the Corresponding Basic Data Container, if the field is set to extract Basic Data in the Target Table Definition. If this flag is set, and the Target Table get a Basic Data Validation Error, it is possible to automatically upload the missing values into the Basic Data Container.

When Basic Data Extraction function is executed, it extracts all uniqe values that does not yet exist in the Basic Data Container. It looks through all columns, that has been defined for Basic Data Extraction and create new records, in the Basic Data Container with these values for the Basic Data Target Table, defined for that field.

Approve/Unapprove Data

If there are no errors in the records, the next step in the Validation process, is to set the status to Approved. This makes the records to appear into the Deployment Container. Unapproved record are not appeared in the deployment container

Bulk Delete

In the Bulk Delete function, it is possible to Delete a large number of data based on a number of filter field values. This way it is easy to correct errors if any has occurred.

Before Executing the Deletion, the number of records, that are filtered and will be deleted, is presented in the statistic field.