Overview of Hub Loader Transforms

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1: Overview

Hub Loader transforms are used to modify or generate data tables dynamically within SmartHub. They support a variety of transformation types to help users reshape, enrich, and organize survey data for downstream analysis. Each transform operates as part of a transform block and is referenced through the hubLoader configuration.

These transforms enable advanced table logic, custom dimension calculations, and data pivoting—all without altering the original survey data.

2: Accessing Transforms

To define and apply a transform, use the following structure:

transform #yourTransformName {
  // your transformation logic here
}
config hubLoader {
  transforms: @yourTransformName
}

You can apply a single transform or a list of multiple transforms by referencing them with @.

3: Types of Transforms

Hub Loader supports several transform types, each tailored for a specific use case:

Transform Type	Description
dimensionGroup	Creates a set of related tables from a group of dimensions. Often used for thematic analysis.
table dimension	Calculates average scores for each dimension and creates dimension-specific data.
table unPivot	Reorganizes a list of variables into a pivot-style table, where each row represents a single variable.
table	Alters or updates an existing table by creating new variables or updating existing ones.

Each transform type has a different set of properties and behaviors. You can combine them for complex workflows.

4: When to Use Hub Loader Transforms

Use transforms when you need to:

• Calculate and normalize scores across dimensions.

• Pivot variables into structured tables for export.

• Modify or extend the structure of existing datasets.

• Recategorize values using custom logic.

• Automate post-survey data preparation workflows.

5: Configuration Example

config hubLoader {
  transforms: (@transformResponse, @transformAnalytics
}

This structure tells SmartHub to apply the transformResponse and transformAnalytics transform blocks during data loading.