Output Columns Tab Terminology

The Output Columns tab enables you to review and configure the structure of your dataset output before saving. It provides control over column names, data types, formatting rules, and transformation settings applied to query results.

This tab helps you:

Standardize data presentation across dashboards and reports
Improve readability with clear, business-friendly column names
Ensure accuracy by validating data types and formats before publishing
Prepare data for downstream analytics and visualizations

Refer to Create Dataset to navigate to the General Tab and access the Output Columns section.

Output Columns Interface

The Output Columns grid displays metadata for all columns returned by the dataset query.

Output Columns

Each column includes the following configurable fields:

Name
Data Type
Alias
Format
Overwrite Data Type

Column Definitions

Name

Purpose

The Name field displays the original field name as retrieved from the datasource or query result. This helps you trace the column back to the underlying table or query structure.

Key Points

Reflects the actual database or query column name
Remains unchanged unless you modify the Alias field
Serves as the technical identifier for the column

Data Type

Purpose

The Data Type field indicates the system-detected data type of the column, ensuring that calculations, filters, and visualizations behave correctly.

Supported Data Types

String – Text values
Integer – Whole numbers
Decimal – Numbers with decimal points
Date – Date values without time
Boolean – True/false values
Timestamp – Date and time values

Key Points

Automatically derived from the datasource schema
Critical for accurate aggregations, comparisons, and visualizations
Can be overridden using the Overwrite Data Type field

Alias

Purpose

The Alias field allows you to assign an alternate, business-friendly name to a column without modifying the original datasource structure.

Common Use Cases

Improve readability for business users
Apply consistent naming conventions across reports and dashboards
Standardize column names across multiple datasets
Create user-friendly labels for technical field names

How to Modify an Alias

Double-click the value in the Alias column
Enter the new, business-friendly name
Click Preview Result to validate that the updated name appears correctly in the output

Key Points

The original column name remains unchanged in the datasource
Aliases appear in reports, dashboards, and exports
Changes take effect immediately after saving the dataset

Format

Purpose

The Format field applies transformation expressions to String values, ensuring text is presented in a clean and consistent manner.

Availability

Format transformations are available only for String data type columns.

How to Apply Format

Click the expression (Σ) icon in the Format column for the desired field
Select the appropriate transformation from the available options
Click Preview Result to confirm the output meets your requirements

Available Format Options

Lowercase – Converts all characters to lowercase
Example: ProductID = PRD10000 → prd10000
Uppercase – Converts all characters to uppercase
Example: ProductName = Levis Shirts 18 → LEVIS SHIRTS 18
charAt(position) – Extracts the character at the specified position (index starts from 0)
Example: Brand = Levis → charAt(0) → L
substr(start, length) – Extracts a substring starting from a given position for the specified length
Example: ProductType = Clothing → substr(0, 5) → Cloth

Key Points

Format transformations apply at query execution time
Multiple columns can have different format rules
Original data in the datasource remains unchanged
Always preview results before saving to ensure expected output

Overwrite Data Type

Purpose

The Overwrite Data Type field allows you to redefine the data type of a column by selecting a different type from the dropdown list.

When to Use

The system-detected data type is incorrect
A column requires conversion for reporting or visualization purposes
Data type adjustments are needed for calculations or aggregations
You need to ensure compatibility with downstream systems

Common Examples

Convert a String column containing dates to Date format
Change a Decimal column to Integer for simplified display
Adjust a Timestamp column to Date to remove time components
Convert numeric String values to Integer or Decimal for calculations

How to Apply Overwrite Data Type

Click the Overwrite Data Type dropdown for the desired column
Select the target data type from the list
Click Preview Result to validate the conversion before saving

Key Points

Ensure the source data is compatible with the target data type
Incompatible conversions may result in null values or errors
Preview results carefully to verify successful conversion
Original datasource data types remain unchanged

Reset

Purpose

The Reset function discards all changes made in the Output Columns tab and returns the configuration to its original state.

What It Affects

Reset undoes all modifications applied in the Output Columns tab, including:

Alias updates
Format transformations
Overwrite Data Type selections

When to Use

You want to start over from the original query output configuration
You are not satisfied with the current changes and prefer to revert completely
You need to undo multiple changes quickly without manual reversal

Key Points

Reset is irreversible once confirmed
Only affects Output Columns settings; other dataset configurations remain unchanged
Consider reviewing changes before using Reset to avoid losing intended modifications

Summary

The Output Columns tab ensures that your dataset output is:

Properly structured with clear, meaningful column names
Correctly typed for accurate calculations and visualizations
Consistently formatted for business user comprehension
Ready for analysis across dashboards, reports, and downstream applications

By carefully reviewing and configuring this tab, you can deliver high-quality, analysis-ready datasets that meet the needs of your organization.