Data Mapping — Examples

Example 1 — Normalize an External API Response

An e-commerce API returns product data with inconsistently cased field names and string-typed prices. Map them to a clean internal schema.

{
  "node_type": "DataMapping",
  "name": "NormaliseProduct",
  "config": {
    "source_output": "FetchProductAPI",
    "mappings": [
      { "source_field": "ProductName",    "target_field": "name",     "transform": "trim" },
      { "source_field": "ProductSKU",     "target_field": "sku",      "transform": "uppercase" },
      { "source_field": "UnitPrice",      "target_field": "price",    "transform": "toFloat" },
      { "source_field": "StockQty",       "target_field": "stock",    "transform": "toInt",    "default_value": 0 },
      { "source_field": "IsActive",       "target_field": "active",   "transform": "toBoolean" }
    ]
  }
}

Outcome: mapped_data contains { name, sku, price, stock, active } with correct types, ready for database insertion.

Example 2 — Map CRM Fields to Invoice Schema

Pull a contact from the CRM and map it to the structure expected by the invoice generation node.

{
  "node_type": "DataMapping",
  "name": "MapInvoiceRecipient",
  "config": {
    "source_output": "GetCRMContact",
    "mappings": [
      { "source_field": "contact.firstName",      "target_field": "billToFirstName" },
      { "source_field": "contact.lastName",       "target_field": "billToLastName" },
      { "source_field": "contact.emailAddress",   "target_field": "billToEmail",     "transform": "lowercase" },
      { "source_field": "contact.billingAddress.line1", "target_field": "addressLine1", "default_value": "N/A" },
      { "source_field": "contact.billingAddress.city",  "target_field": "city",          "default_value": "N/A" },
      { "source_field": "contact.billingAddress.postCode","target_field": "postCode",     "default_value": "" }
    ]
  }
}

Outcome: A clean billing address object ready to be passed to the PDF invoice generator node.

Example 3 — Legacy Date Format Conversion

An older system exports dates as MM/DD/YYYY strings. Convert them to ISO 8601 for storage and downstream processing.

{
  "node_type": "DataMapping",
  "name": "ConvertDates",
  "config": {
    "source_output": "ParseLegacyRecord",
    "mappings": [
      {
        "source_field": "contractStartDate",
        "target_field": "startDateISO",
        "transform": "toDate"
      },
      {
        "source_field": "contractStartDate",
        "target_field": "startDateDisplay",
        "transform": "dateFormat",
        "transform_params": { "format": "dd MMMM yyyy" }
      }
    ]
  }
}

Outcome: Both a machine-readable ISO date and a human-readable display date are produced from the same source field.

Example 4 — Extract Nested Fields from a Deep JSON Response

A payment gateway returns a deeply nested response. Extract the key fields into a flat object for the accounting system.

{
  "node_type": "DataMapping",
  "name": "FlattenPaymentResponse",
  "config": {
    "source_output": "ProcessPaymentGateway",
    "mappings": [
      { "source_field": "transaction.id",                   "target_field": "transactionId" },
      { "source_field": "transaction.status",               "target_field": "status",       "transform": "uppercase" },
      { "source_field": "transaction.amount.value",         "target_field": "amountCents",  "transform": "toInt" },
      { "source_field": "transaction.amount.currency",      "target_field": "currency",     "transform": "uppercase" },
      { "source_field": "transaction.card.last4",           "target_field": "cardLast4" },
      { "source_field": "transaction.timestamps.authorized","target_field": "authorizedAt", "transform": "toDate" }
    ]
  }
}

Outcome: A flat mapped_data object with six clean fields, ready for the accounting database insert node.

Example 5 — Phone Number Sanitisation with Regex

Users enter phone numbers in various formats. Strip all non-digit characters to produce a standardised E.164-ready digit string.

{
  "node_type": "DataMapping",
  "name": "SanitisePhoneNumber",
  "config": {
    "source": "{@ $input.current }",
    "mappings": [
      {
        "source_field": "phoneNumber",
        "target_field": "phoneDigits",
        "transform": "regexReplace",
        "transform_params": {
          "pattern": "[^0-9]",
          "replacement": ""
        },
        "default_value": ""
      }
    ]
  }
}

Outcome: Input "(512) 555-1234" becomes "5125551234". Input "+1-800-FLOWERS" becomes "1800" (letters are stripped). Empty input produces "" due to default_value.