Replace null values

Description

he Replace Null Values activity is designed to automatically fill missing or empty entries in your dataset, helping ensure data completeness and integrity for downstream processing.

It works by allowing users to define a list of specific columns where null (null) or blank ("") values may exist. For each such column, a default replacement value can be configured along with the expected data type. The activity then traverses each row and substitutes any null or blank cell with the appropriate typed value.

Supported data types include:

• String – e.g., "Unknown", "N/A", "Not Provided"
• Number – e.g., 0, -1, or any decimal/integer value
• Date – e.g., "2023-01-01" (must be parsable in common formats)
• Boolean – e.g., true or false

This activity is especially useful in cases where:

• Downstream activities (like filters, aggregations, or joins) depend on non-null values.
• You’re preparing datasets for machine learning, reporting, or transformation pipelines.
• Clean, uniform inputs are essential — such as dashboards or business rules relying on default fallbacks.

By replacing incomplete data in a structured and type-safe manner, this activity simplifies pre-processing while preserving schema consistency.

Tip: Only columns with explicitly defined mappings will be processed. All other columns remain unchanged.

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Input

Field	Description
Data	Tabular data from the previous activity.

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Output

Field	Description
Data	The transformed table with null/empty values replaced in specified columns.

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Configuration Fields

Field	Required	Description
Columns Map	Yes	List of column mappings to fill. Each map defines the column, value, and type.

Each Columns Map item includes:

Subfield	Required	Description
Column Name	Yes	Name of the column to fill if null or empty.
Data Type	Yes	The type of value (String, Number, Date, Boolean)
Value	Yes	The default value to use when cell is null/empty

Sample Input

Name	Country	Age	JoinedOn	IsActive
Alice	India	30	2023-04-10	true
Bob
Carol	UK	22	2023-02-18	false

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Sample Configuration

Column Name	Data Type	Value
Country	String	Unknown
Age	Number	0
JoinedOn	Date	2023-01-01
IsActive	Boolean	true

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Sample Output

Name	Country	Age	JoinedOn	IsActive
Alice	India	30	2023-04-10	true
Bob	Unknown	0	2023-01-01	true
Carol	UK	22	2023-02-18	false

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