Map Function

The Map function selects which fields to include in your output, optionally renaming them. It's essential for controlling your data structure, reducing payload size, and creating clean outputs.

How It Works

Map processes each row and:

• Includes only selected fields in the output
• Renames fields according to your mappings
• Reorders fields based on the mapping sequence
• Removes all other fields

Unlike Transform, Map doesn't create calculated fields - it simply selects and renames existing fields.

Adding a Map

1. Drag Map from the Functions section of the Element Panel
2. Or click on the canvas and select Map from the quick menu
3. Connect it to your data source
4. Click the Map node to configure field mappings

Configuration Panel

Field Mappings

The Map panel displays a mapping table with two columns:

Source Field	Output Field
Original field name	New field name (or same)

Adding Field Mappings

Method 1: Add Individual Fields

1. Click Add Field
2. Select a source field from the dropdown
3. Enter the output field name (defaults to source name)

Method 2: Add All Fields

1. Click Add All Fields
2. All upstream fields are added with their original names
3. Remove or rename fields as needed

Method 3: Drag and Drop

1. Drag fields from the available fields list
2. Drop them into the mapping area
3. Reorder by dragging within the list

Field Selection

Available Fields Panel

The left side shows all available fields from upstream nodes:

• Field name
• Data type icon
• Source node (if from multiple sources)

Click a field or drag it to add it to your mapping.

Selected Fields Panel

The right side shows your current mapping:

• Source field name
• Arrow indicator
• Output field name (editable)
• Remove button

Renaming Fields

To rename a field:

1. Add the field to your mapping
2. Click the output field name
3. Type the new name
4. Press Enter or click away

Example:

Source	Output
first_name	FirstName
last_name	LastName
email_address	Email
created_date	CreatedAt

Reordering Fields

Field order in the output matches the mapping order:

1. Drag a field row up or down
2. Or use the move buttons (if available)

The first field in your mapping is the first field in output.

Common Use Cases

Selecting a Subset of Fields

Scenario: Your entity has 50 columns, but you only need 5.

Before Map:

ID, FirstName, MiddleName, LastName, Email, Phone, Address1, Address2,
City, State, Zip, Country, DateOfBirth, SSN, AccountNumber, Balance,
CreditLimit, Status, CreatedDate, ModifiedDate, CreatedBy, ModifiedBy,
Department, Manager, Title, HireDate, TermDate, Salary, Bonus, ...

Map Configuration:

Source	Output
ID	ID
FirstName	FirstName
LastName	LastName
Email	Email
Status	Status

After Map:

ID, FirstName, LastName, Email, Status

Renaming for External Systems

Scenario: Destination system expects specific field names.

Map Configuration:

Source	Output
CustomerID	cust_id
CustomerName	name
EmailAddress	email
PhoneNumber	phone
CreateDate	created_at

Standardizing Field Names

Scenario: Different sources use different naming conventions.

Source 1 fields: first_name, last_name, email_addr Source 2 fields: FirstName, LastName, EmailAddress

Map for both sources:

Source	Output
(varies)	FirstName
(varies)	LastName
(varies)	Email

Creating API Responses

Scenario: API should return specific JSON structure.

Map Configuration:

Source	Output
product_id	id
product_name	name
unit_price	price
quantity_in_stock	stock
is_available	available

Output JSON:

{
  "id": "PRD-001",
  "name": "Widget",
  "price": 29.99,
  "stock": 150,
  "available": true
}

Removing Sensitive Data

Scenario: Exclude sensitive fields before sharing.

Original fields: ID, Name, SSN, AccountNumber, Balance

Map Configuration:

Source	Output
ID	ID
Name	Name
Balance	Balance

Output: ID, Name, Balance (SSN and AccountNumber removed)

Field Naming Guidelines

Allowed Characters

Output field names:

• Letters (a-z, A-Z)
• Numbers (0-9)
• Underscores (_)
• Should start with a letter

Naming Conventions

camelCase:

firstName, lastName, orderDate, totalAmount

PascalCase:

FirstName, LastName, OrderDate, TotalAmount

snake_case:

first_name, last_name, order_date, total_amount

kebab-case: (not recommended for fields)

first-name, last-name, order-date, total-amount

Best Practices for Names

Be descriptive:

// Good
CustomerName, OrderTotal, ShipDate

// Avoid
CN, OT, SD

Be consistent:

// Pick one style
firstName, lastName, emailAddress  // All camelCase
OR
FirstName, LastName, EmailAddress  // All PascalCase

Avoid spaces and special characters:

// Good
TotalAmount, FullName, EmailAddress

// Avoid
"Total Amount", Full-Name, Email@Address

Map vs. Transform

Feature	Map	Transform
Select fields	Yes	No
Rename fields	Yes	No
Reorder fields	Yes	No
Calculate new values	No	Yes
Modify values	No	Yes
Use expressions	No	Yes

Use Map when:

• You only need specific fields
• You need to rename fields
• You're preparing data for export
• You want to clean up your schema

Use Transform when:

• You need calculated fields
• You need to modify values
• You need to use functions or expressions

Use both together:

1. Transform to create calculated fields
2. Map to select and rename the final output

Working with Multiple Sources

After a Merge or Append:

• Fields may have prefixes (e.g., source1.ID, source2.ID)
• Use Map to select which version to keep
• Rename to remove prefixes

Example after Merge:

Source	Output
orders.OrderID	OrderID
orders.CustomerID	CustomerID
customers.CustomerName	CustomerName
customers.Email	Email
orders.OrderDate	OrderDate
orders.Total	OrderTotal

Preserving Data Types

Map preserves the original data type of each field:

• Text fields remain text
• Numbers remain numbers
• Dates remain dates
• Booleans remain booleans

If you need to convert types, use Transform before or after Map.

Empty and Null Values

Map behavior for special values:

• Null values remain null in output
• Empty strings remain empty
• Missing fields cause an error (field must exist)

If a field might not exist, add it conditionally or use Transform with COALESCE first.

Performance Considerations

Select Early

Apply Map early in your flow to:

• Reduce data volume for downstream operations
• Improve performance of merges and transforms
• Minimize memory usage

Example:

[Entity: All 50 columns] → [Map: Select 5] → [Transform] → [Filter] → [Output]

Field Count Impact

More fields = more processing:

• Large row widths slow down operations
• Network transfer increases with field count
• Storage requirements grow

Select only the fields you actually need.

Troubleshooting

"Field not found"

Cause: Source field doesn't exist in upstream data.

Solutions:

• Check spelling of field name
• Verify the field exists in the source
• If field comes from a join, ensure it's included

"Duplicate field name"

Cause: Two mappings have the same output name.

Solutions:

• Give each output field a unique name
• Remove duplicate mappings
• Rename one of the conflicting fields

"Empty output"

Cause: No field mappings defined.

Solutions:

• Add at least one field mapping
• Use "Add All Fields" and then remove unwanted ones

Fields in Wrong Order

Cause: Mapping order determines output order.

Solutions:

• Drag fields to reorder in the mapping panel
• Remove and re-add fields in desired order

Patterns and Examples

API Response Preparation

Scenario: Format data for a REST API response.

Map Configuration:

Source	Output
product_id	id
product_name	name
description	desc
unit_price	price
inventory_count	inventory
category_name	category
is_active	active
image_url	image

Database to Spreadsheet

Scenario: Export to a user-friendly spreadsheet.

Map Configuration:

Source	Output
EMP_ID	Employee ID
FNAME	First Name
LNAME	Last Name
EMAIL_ADDR	Email
HIRE_DT	Hire Date
DEPT_NAME	Department
MGR_NAME	Manager

Integration Data Mapping

Scenario: Map fields to destination system requirements.

Source fields (SAP):

KUNNR, NAME1, NAME2, ORT01, PSTLZ, LAND1

Map to target system:

Source	Output
KUNNR	CustomerNumber
NAME1	CompanyName
NAME2	ContactName
ORT01	City
PSTLZ	PostalCode
LAND1	CountryCode

Removing Computed Fields

Scenario: After Transform, remove intermediate calculations.

After Transform fields:

ProductID, Name, Price, Quantity, Subtotal, TaxRate,
TaxAmount, Discount, DiscountAmount, Total

Map to final output:

Source	Output
ProductID	ProductID
Name	Name
Quantity	Quantity
Total	Total

Next Steps

• Transform Function - Add calculated fields
• Filter Function - Filter rows based on conditions
• Merge Function - Combine data from multiple sources
• Building Flows - Complete workflow guide