Data Mapping
Map and transform fields from a source object to a new target object using 16+ configurable built-in transforms.
The Data Mapping node takes a source data object and produces a new target object by applying a set of user-defined mapping rules. Each rule selects a source field (using dot-notation or array indexing), optionally transforms its value using one of 16+ built-in transforms, and writes the result to a target field. The output mapped_data object contains only the explicitly mapped fields.
Key Capabilities
- Map fields from any source: workflow input, a named node output, or an inline config object
- Dot-notation and array indexing for deep source field access (
user.address.city,items[0].price) - 16+ built-in transforms: case conversion, type coercion, base64, regex, date formatting, number formatting, and more
- Default values for missing or null source fields
- Multiple mapping rules per node — build an entire output DTO in one step
- Output available as
output.NodeName.mapped_datafor downstream nodes - Non-destructive — the original source object is never modified
Business Benefits
Data Mapping eliminates the need to write custom transformation code for routine field mapping tasks. ETL pipelines, API integrations, and CRM-to-ERP data flows often require dozens of field mappings with simple transforms. Instead of a CodeExecute node with manual JavaScript, Data Mapping provides a declarative, auditable configuration that non-developers can understand and maintain.
The 16+ built-in transforms cover the majority of real-world requirements: normalising text case, parsing dates, reformatting phone numbers via regex, converting numeric strings to integers, and encoding data for downstream APIs. The default_value fallback ensures robust handling of incomplete source data.
Common Use Cases
| Scenario | How Data Mapping Helps |
|---|---|
| Normalize API responses | Map an external API's field names to your internal schema, applying type coercions and case transforms in one step. |
| CRM to invoice fields | Map contact.fullName to billTo.name, contact.email to recipient, applying trim and lowercase to the email. |
| Legacy data format transform | Convert date strings from MM/DD/YYYY to ISO 8601 using the dateFormat transform. |
| Extract nested fields | Flatten a deeply nested response object into a flat output record for downstream database inserts. |
| Build output DTOs | Construct the exact payload shape required by a webhook or partner API without modifying the source data. |
In This Guide
Configuration
Source options, mappings array structure, and the complete table of all 16+ built-in transforms with parameters.
Input & Output
Output ports, the mapped_data object schema, and how to reference mapped fields from downstream nodes.
Examples
Five examples: API normalisation, CRM mapping, date reformatting, nested field extraction, and DTO construction.