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Creates new computed fields by applying formula expressions or raw SQL to your data. Use it for calculations, string operations, conditionals, and any rule-based transformation that doesn’t require an LLM.

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Configuration

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Formula Transforms

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SQL Transforms

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How It Works

1

Add field mappings

Define one or more new fields. For each, provide a name and either a formula or SQL expression.
2

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}}.
3

Chain transforms

Later mappings within the same node can reference fields created by earlier mappings.

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Output

New computed columns are added to each record, named as {virtual_object_name}.{field_name}. These become available as Mentions in downstream nodes.

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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

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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
  • 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