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Edit Fields

What it does: Transforms and cleans up data by renaming fields, converting data types, and applying validation rules.

Perfect for: Data cleaning • API response processing • Form data preparation • Field standardization

  • Rename fields - Change field names to match your needs
  • Convert data types - Turn strings into numbers, dates, etc.
  • Clean values - Remove extra spaces, fix formatting
  • Add validation - Ensure data meets your requirements
graph LR
    A[🌐 Raw Data] --> B[✏️ Edit Fields]
    B --> C[📊 Clean Data]

    style A fill:#e1f5fe
    style B fill:#e8f5e8
    style C fill:#f3e5f5

Simple flow: Raw data → Edit Fields node → Clean, formatted data

Rename Fields

{"action": "rename", "from": "user_name", "to": "username"}

Convert Types

{"action": "convert", "field": "age", "type": "number"}

Clean Values

{"action": "transform", "field": "email", "function": "toLowerCase"}

Add New Fields

{"action": "add", "field": "timestamp", "value": "{{now}}"}

Before (messy data):

{
"user_name": "Alice Smith",
"age": "28",
"email": "ALICE@COMPANY.COM"
}

After (clean data):

{
"username": "Alice Smith",
"age": 28,
"email": "alice@company.com"
}

What happened: Renamed “user_name” to “username”, converted age from text to number, and made email lowercase.

Clean web scraped data - Fix messy field names and data types from websites Prepare API data - Format data before sending to external services Process form submissions - Clean and validate user input data Standardize data - Make data consistent across different sources

Field not found errors: Check that field names match exactly (case-sensitive) Type conversion fails: Make sure the data can actually be converted (e.g., “abc” can’t become a number) Operations not working: Verify your JSON syntax is correct

Related nodes: FilterMergeHTTP Request

Common workflows: Data Processing PatternsWeb Extraction WorkflowsAPI Integration

Learn more: Data Transformation GuideMulti-Step Workflows