Current Zoho Flow & Creator Capabilities:

• Zoho Flow does not natively provide a "skip empty values" option when updating Creator records
• The Zoho Creator.updateRecords Deluge task replaces field values completely - empty values will overwrite existing data
• Conditional field mapping must be implemented through Flow decision blocks or custom functions

Flow Structure Pattern:

1. Trigger (Webhook/Schedule/Form Submission)
   ↓
2. Get Source Data
   ↓
3. Decision Block: Check Field 1
   ├─ If Not Empty → Add to Update Map
   └─ If Empty → Skip
   ↓
4. Decision Block: Check Field 2
   ├─ If Not Empty → Add to Update Map
   └─ If Empty → Skip
   ↓
5. [Repeat for all fields]
   ↓
6. Decision Block: Check if Update Map has data
   ├─ If Yes → Update Creator Record
   └─ If No → Log "No updates needed"

Optimized Code Implementation:

// Comprehensive selective update function
void updateCreatorRecordSelectively(int recordId, Map sourceData, string appName, string formName)
{
    try {
        // Initialize update map with only non-empty values
        updateMap = Map();
        
        // Define field mapping with validation
        fieldMappings = {
            {"source_field": "Task_Name", "target_field": "Name", "required": false},
            {"source_field": "Task_Status", "target_field": "Status", "required": false},
            {"source_field": "Due_Date", "target_field": "Due_Date", "required": false},
            {"source_field": "Priority", "target_field": "Priority", "required": false}
        };
        
        // Process each field mapping conditionally
        for each mapping in fieldMappings {
            sourceField = mapping.get("source_field");
            targetField = mapping.get("target_field");
            isRequired = mapping.get("required");
            
            sourceValue = sourceData.get(sourceField);
            
            // Comprehensive empty value checking
            if(isValidValue(sourceValue, isRequired)) {
                updateMap.put(targetField, sourceValue);
                info "Including field: " + targetField + " with value: " + sourceValue;
            } else {
                info "Skipping field: " + targetField + " (empty or invalid value)";
            }
        }
        
        // Only proceed with update if we have fields to update
        if(updateMap.size() > 0) {
            criteria = "ID == " + recordId;
            response = zoho.creator.updateRecords(
                "your_account", 
                appName, 
                formName, 
                criteria, 
                updateMap, 
                Map(), 
                "creator_connection"
            );
            
            if(response.get("code") == 3000) {
                info "Record updated successfully. Fields updated: " + updateMap.keys().toString();
                return {"status": "success", "updated_fields": updateMap.keys(), "record_id": recordId};
            }
        }
        
    } catch (e) {
        info "Error in updateCreatorRecordSelectively: " + e.toString();
        return {"status": "error", "message": e.toString()};
    }
}

// Enhanced value validation function
boolean isValidValue(string value, boolean isRequired)
{
    // Handle null values
    if(value == null) {
        return isRequired ? false : false;
    }
    
    // Handle empty strings
    if(value.toString().trim() == "") {
        return isRequired ? false : false;
    }
    
    // Handle specific "empty" indicators
    if(value.toString().toLowerCase() == "null" || 
       value.toString().toLowerCase() == "undefined" ||
       value.toString() == "0" && !isRequired) {
        return false;
    }
    
    return true;
}

✅ Recommended Approach

• Pre-Update Validation: Always validate source data before mapping
• Null vs Empty Handling: Check for both null and empty string conditions
• Data Type Considerations: Handle different field types (text, number, date) appropriately
• Batch Processing: Group related field updates for efficiency
• Error Handling: Implement comprehensive try-catch blocks

Batch Processing Strategy:

• Process updates in batches of 200 records maximum
• Implement retry logic for failed batches
• Use asynchronous processing for large datasets
• Cache lookup data to avoid repeated queries

Common Issues and Solutions:

• Empty Values Still Overwriting: Check for whitespace-only strings, validate null vs empty string handling
• Performance Issues: Implement batch processing, use bulk update operations, cache lookup data
• Flow Timeout Issues: Break large operations into smaller chunks, use asynchronous processing
• Data Type Conversion Errors: Add explicit type validation, handle date format conversions

1. Assess Current Flow: Review your existing Flow configuration and identify fields that need conditional updates.
2. Implement Strategy: Choose between Flow conditional mapping or Deluge-based selective updates based on complexity.
3. Test Thoroughly: Create test scenarios with partial data to validate data preservation.
4. Monitor Performance: Track update success rates and execution times
5. Document Process: Create documentation for your team on the selective update methodology