⚙️ Introduction

Mapping is a core step in ensuring that external data can be correctly interpreted and processed by DJUST. It defines how external fields (from a CSV file or REST API response) are linked to DJUST’s internal data model.

Without mapping, DJUST cannot understand where to place imported data or how to structure exported data.

This configuration is required for every import and export job to function properly.

• Import jobs: map source fields to DJUST’s fields.
• Export jobs: map DJUST's internal fields to the expected output for external systems (eg. the API field of a payload)

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⚙️ Functional behavior

You can manage mapping settings directly from DJUST Back Office in the Data Hub for each Job Configuration

When configuring a job, you must provide the name of each input field (API field or CSV column header) and link it to the corresponding DJUST column name in the target entity model.

Example: If your system has a field called "Supplier Reviews", and DJUST expects supplierRating, enter Supplier Reviews next to supplierRating.

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⚙️ API Mapping

When mapping data fetched via the API Connector from a JSON payload, additional syntax and parameters can be used to accurately extract and align nested or structured fields.

Access Nested Values

Define Data Type Behavior

For each mapped field, you must specify:

• JSON VALUE: if the field is a string, boolean, number, etc.
• JSON VALUE ARRAY: if the field is a list of values. Two options:
  • FIRST: keep only the first value
  • CONCATENATE TO STRING: merge array values into one string

Payload Structure

The JSON payload must follow a specific structure expected by DJUST. This structure ensures that data is correctly interpreted and mapped to DJUST’s internal model/

For example, when importing order data, the root object should represent the Order Logistic, and it should contain a nested array of Order Lines.

JSON

{
   "elements":[
      {
         "orderLogisticId":"12345",
         "status":"CREATED",
         "orderLines":[
            {
               "productId":"ABC123",
               "quantity":2,
               "unitPrice":19.99
            }
         ]
      },
      {
         "orderLogisticId":"67890",
         "status":"SHIPPED",
         "orderLines":[
            {
               "productId":"XYZ789",
               "quantity":1,
               "unitPrice":49.90
            }
         ]
      }
   ],
   "paging":{
      "pageNumber":1,
      "pageSize":50,
      "totalPages":2,
      "totalRecords":100
   }
}

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⚙️ Transcoding

The transcoding system lets users define mappings that convert an input value to an output value for a given import job

Key use cases

• When configuring an import job, users may want to standardize incoming values so that data imported from external systems matches the naming or categorization conventions of the platform (e.g., normalizing Product or Variant names).
• When importing external identifiers, users may need to translate external system values into platform-compatible values (e.g., converting an external classification identifier to the expected internal one).

Business rules

• When a conversion is active for a mapping, the output value automatically replaces the input value.
• An output value can be associated with multiple input values.
• An input value can be associated with only one output value.
• The input value can be an empty string but cannot be null.
• Output values must be non-empty and non null.
• Custom Fields cannot use the transcoding conversion.

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📋 API quicklinks

• Create one or several transcodings for a specific job: POST /v2/mapper/job/{jobId}/transcodings
• Fetch one transcoding from a specific jobGET /v2/mapper/job/{jobId}/transcodings/{id}
• Fetch a list of transcodings from a specific job GET /v2/mapper/job/{jobId}/transcodings
• Update a list of transcodings from a specific jobPUT /v2/mapper/job/{jobId}/transcodings
• Detele a transcoding for a specific jobDELETE /v2/mapper/job/{jobId}/transcoding/{id}