Create a Mapping Template

A mapping template tells Datatrax how to translate the columns in your CSV file into fields on a Salesforce object. You define it once and reuse it for every load of that file shape.

Create the mapping record

1. In your Salesforce org, click the App Launcher (the grid icon in the top-left).
2. Search for Datatrax and open the app.
3. Click the Data Load Mappings tab.
4. Click New.
5. Fill in these fields before saving:
   Name

   A unique, descriptive identifier. Example: Account_CustomerMaster. This name is referenced in each Data Load Request.

   Object API Name

   The API name of the Salesforce object you're loading data into. Example: Account, Contact, Opportunity. Set this before saving — the Column Mapping Builder needs it to load the field list.

   Operation

   Choose Insert (new records only), Update (existing records), Upsert (insert or update based on an external ID), or Delete.

   External ID Field

   Required for Upsert only. The API name of the field used to match records. Example: External_Id__c.
6. Leave Active unchecked for now and click Save. You'll activate it after building the column mapping.

Build your column mapping

After saving, the record page displays the Column Mapping Builder. This is the recommended way to define your mapping — it shows a searchable dropdown of every field on the target object and automatically sets the correct data type for each one.

1. In the Column Mapping Builder card on the record page, you'll see a table with three columns: CSV Column Header, Salesforce Field, and Type.
2. For each column in your CSV:
   • Type the exact CSV column header into the CSV Column Header field (capitalization and spaces must match your file exactly).
   • Select the target Salesforce field from the Salesforce Field dropdown. The dropdown is searchable and shows both the field label and API name.
   • The Type column auto-fills based on the Salesforce field's data type. You can override it if needed.
3. Click Add Row to add more columns.
4. Click Save Mapping when all rows are complete. The builder writes the JSON to the Column Mappings field automatically.
5. Edit the record and check the Active checkbox, then save. The mapping is now available for use in Data Load Requests.

Automatic type conversion

Non-string fields are automatically transformed to match what Salesforce expects before loading. You don't need to pre-format your data — Datatrax normalizes each value based on the field type in your mapping.

Examples:

# Date — both of these produce 2024-01-15 in Salesforce
"Close Date": "2024-01-15"
"Close Date": "1/15/2024"

# DateTime — all of these produce 2024-01-15T14:30:00Z in Salesforce
"Created At": "2024-01-15T14:30:00Z"
"Created At": "2024-01-15T14:30:00+05:00"
"Created At": "1/15/2024T14:30:00Z"

# DateTime — date-only defaults to midnight (2024-01-15T00:00:00Z)
"Created At": "1/15/2024"

# Boolean — all of these produce true in Salesforce
"Is Active": "Yes"
"Is Active": "TRUE"
"Is Active": "1"
"Is Active": "Y"

Advanced: editing the JSON directly

If you need to edit the mapping outside of Salesforce (for example, to version-control it or bulk-create mappings via API), the Column Mappings field stores JSON in this format:

{
  "Customer Name":  {"field": "Name",           "type": "string"},
  "Email Address":  {"field": "Email__c",        "type": "string"},
  "Annual Revenue": {"field": "AnnualRevenue",   "type": "currency"},
  "Is Partner":     {"field": "IsPartner",       "type": "boolean"},
  "Close Date":     {"field": "CloseDate",       "type": "date"}
}

Each key is a CSV column header. Each value is an object with:

• field — the Salesforce field API name
• type — one of string, boolean, date, datetime, double, currency, integer, percent

Unmapped columns are ignored. If you edit the JSON directly, the Column Mapping Builder will reflect your changes the next time you open the record.