Configuration
| Setting | Description |
|---|---|
| Transforms | Formula-based field mappings (optional) |
| SQL Transforms | DuckDB SQL-based field mappings (optional) |
| Virtual Object Name | Namespace prefix for output fields (default: transform) |
Formula Transforms
| Setting | Description |
|---|---|
| Field Name | Name for the new computed field |
| Formula | Expression using {{Integration.Entity.Field}} syntax |
SQL Transforms
| Setting | Description |
|---|---|
| Field Name | Name for the new computed field |
| SQL Expression | DuckDB SQL expression |
How It Works
Add field mappings
Define one or more new fields. For each, provide a name and either a formula or SQL expression.
Reference upstream fields
Use
{{Integration Label.Entity.Field}} syntax to reference existing fields in your formulas. Virtual fields from earlier nodes use {{virtual.node_name.field_name}}.Output
New computed columns are added to each record, named as{virtual_object_name}.{field_name}. These become available as Mentions in downstream nodes.
Example
Calculate deal value and categorize:- Field 1:
deal_value={{Salesforce.Opportunity.Amount}} * {{Salesforce.Opportunity.Probability}} - Field 2 (SQL):
tier=CASE WHEN deal_value > 100000 THEN 'Enterprise' WHEN deal_value > 10000 THEN 'Mid-Market' ELSE 'SMB' END
Best Practices
- Use formulas for simple arithmetic and string operations
- Use SQL transforms for complex logic (CASE statements, string functions, date math)
- Give fields descriptive names — they appear as Mentions throughout your workflow
- Later fields can reference earlier fields defined in the same Transform node
Related Nodes
- Data Normalization — uses an LLM for intelligent cleaning instead of rule-based formulas
- Aggregation — computes summary statistics (SUM, AVG, COUNT) across groups
- Code Execution — run custom Python code for transformations that go beyond formulas and SQL