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Computes aggregation functions (SUM, AVG, COUNT, MIN, MAX, etc.) over grouped data. Typically used after a Group By node to calculate summaries per group.

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Configuration

SettingDescription
AggregationsFormula-based aggregation mappings (optional)
SQL AggregationsSQL-based aggregation mappings (optional)
Virtual Object NameNamespace prefix for output fields (default: aggregation)

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Formula Aggregations

SettingDescription
Field NameName for the aggregated field
FormulaAggregation expression using SUM(), AVG(), COUNT(), MIN(), MAX(), etc.

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SQL Aggregations

SettingDescription
Field NameName for the aggregated field
SQL ExpressionDuckDB SQL aggregation expression

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How It Works

1

Group your data first

Place a Group By node before this one to define how records should be grouped.
2

Define aggregations

Add one or more aggregation fields. Each computes a summary value across the records in each group.
3

Use results downstream

The aggregated fields become available as Mentions in downstream nodes.

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Output

New aggregated columns are added, computed per group. Output fields are named {virtual_object_name}.{field_name}.

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Example

Calculate total revenue and deal count per industry:
  1. Group By → Industry
  2. Aggregation:
    • total_revenue = SUM({{Salesforce.Account.Revenue}})
    • deal_count = COUNT({{Salesforce.Account.Id}})
    • avg_deal_size = AVG({{Salesforce.Account.Revenue}})

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Best Practices

  • Always place a Group By node before this node — aggregation without grouping produces a single result for the entire dataset
  • Nested functions and mathematical operations are supported (e.g., SUM(field) / COUNT(field))
  • Use SQL aggregations for complex expressions
  • Group By — required before Aggregation to define groups
  • Transform — compute per-row fields (not aggregations)
  • Split Group — remove grouping after aggregation