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The Data Mapper node transforms field names between different systems by applying configurable field mappings. It resolves mapping configurations without custom code, translating data formats between your internal system and external applications.

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When to use

  • Map internal fields to external application field names (NetSuite, SAP)
  • Transform incoming data to match your internal system format
  • Apply user-defined field mapping configurations from Config settings
  • Standardize data formats between multiple third-party applications
Data Mapper handles flat field mapping only. For nested objects, arrays, or data type conversion, use the Transform node or Custom Code node.

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How it works

The node renames the fields of an Input object using a Mapping of key-value pairs, where each pair defines a rename relationship ("source_name": "destination_name"). The Direction decides which side of the mapping is matched against the input and which side is used for the output:
  • Source to Destination — the node looks at the input field names, finds the matching keys in the mapping, and writes them out under the mapping values.
  • Destination to Source — the reverse: the input field names match the mapping values, and the output uses the mapping keys.
Only fields present in the mapping are carried into the output — any unmapped input fields are dropped. The mapped object is returned under body. Example (Source to Destination):
Input{"fname": "abc", "lname": "def"}
Mapping{"fname": "firstname", "lname": "lastname"}
DirectionSource to Destination
Output (body){"firstname": "abc", "lastname": "def"}
The input keys fname and lname match the mapping keys, so the values abc and def are re-keyed to firstname and lastname. Switching the direction to Destination to Source would instead expect input keyed by firstname/lastname and produce fname/lname.

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Input parameters

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Input
JSON
required
Source data object whose field names you want to map. Accepts static data or variables from previous nodes.
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Mapping
JSON
required
Field mapping configuration as a JSON object with key-value pairs.
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Direction
dropdown
required
Mapping direction, applied to the Mapping field’s key-value pairs:
  • Source to Destination — input field names match the mapping keys; the output uses the mapping values.
  • Destination to Source — input field names match the mapping values; the output uses the mapping keys.

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Output

The node returns a standard response envelope. The mapped object — with transformed field names — is returned under body. Only fields included in the Mapping appear in the output; unmapped input fields are dropped. 
{
  "status": "Success",
  "node_id": "8",
  "node_name": "Data Mapper",
  "body": {
    "firstname": "abc",
    "lastname": "def"
  }
}

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Adding to your workflow

1

Add Data Mapper node

Drag the Data Mapper node from utility nodes onto your workflow canvas.
2

Configure input data

Provide the source object from a previous node or static data. Input must be a flat JSON object.
3

Define field mapping

Create mapping rules as key-value pairs: {"source_field": "destination_field"}.
4

Select direction

Choose Source to Destination when sending data out, or Destination to Source when receiving data.
5

Test the node

Run the node to verify field transformations.

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Examples

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Map Internal Fields to NetSuite Contact

Transform internal contact data to NetSuite format: Input: 
{
  "fname": "Alice",
  "lname": "Johnson",
  "email_id": "alice@techcorp.com",
  "phone_num": "+1-555-123-4567"
}
Mapping: 
{
  "fname": "firstName",
  "lname": "lastName",
  "email_id": "email",
  "phone_num": "phone"
}
Direction: Source to Destination Output: 
{
  "firstName": "Alice",
  "lastName": "Johnson",
  "email": "alice@techcorp.com",
  "phone": "+1-555-123-4567"
}

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Map SAP Data to Internal Format

Transform incoming SAP vendor data to your internal structure: Input: {{sap_vendor_response}} 
{
  "NAME1": "Acme Corporation",
  "LIFNR": "V001234",
  "LAND1": "US",
  "ORT01": "Chicago"
}
Mapping: 
{
  "vendor_name": "NAME1",
  "vendor_id": "LIFNR",
  "country": "LAND1",
  "city": "ORT01"
}
Direction: Destination to Source Output: 
{
  "vendor_name": "Acme Corporation",
  "vendor_id": "V001234",
  "country": "US",
  "city": "Chicago"
}

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Troubleshooting

ProblemSolution
Missing fields in outputVerify field names in mapping exactly match input (case-sensitive)
Wrong mapping directionCheck direction setting matches your data flow
Empty outputConfirm input is not null and contains at least one mapped field
Nested field errorsUse Transform node for nested objects

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Next steps

Transform node

Run complex JSON transformations.

Custom Code node

Process fields with advanced logic.

Variables

Use mapped data in subsequent nodes.