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If there is not already a transformation block that meets your needs, one option is to write your own. Edlink allows you to write custom transformations in JavaScript/Typescript. These transformations can be used in the pipline just like the standard ones.

Transformations are applied in order and the output of each transformation is passed as the input to the next transformation. This means that the output of your custom transformation could affect the result of, for example, licensing rules.

Creating the block

You can create custom transformations in the Data Pipeline previewer. To do so click the Add Block button in the sidebar. Then select Create A Custom Script from the list of options. This will create a new custom transformation, add it to the pipeline, and open the editor.

Writing the function

When writing a custom transformation you can perform any operation you want on the data. The only requirement is that you return the Dataset object that you are passed. In addition to standard JS operations, you can also use the lodash library. The lodash library is already included and can be accessed via the _ namespace.

Function signature

The function signature for a custom transformation is as follows:

import { Dataset } from 'index';

function transform($ctx: Context, $config: Record<string, any>, $input: Dataset): Dataset {
    // Write your transformation function here.

    return $input;
}

When you open the transformation editor, you will see a function that looks like this. This is the simplest possible transformation. It takes the input dataset and returns it unchanged.

It is vital you use exactly this function signature. If you change the name of the function, the parameters, or even remove the types, the transformation will not work.

The $config parameter is not used currently.

The $ctx paramater contains information about the source, integration, and other metadata.

The $input parameter is the dataset that you will be transforming. It contains Maps of all the entities in your source/integration. It is self-referential and will manage referneces/dependencies for you. It is recommended that you modify this dataset in place and then return it for the best results.

The following are some example transformations that you can use as a starting point for your own.

Change everyone's name to "John Doe"

This transformation loops over all the people in the dataset and sets their first_name to John and their last_name to Doe and their display_name to John Doe.

import { Dataset, Person } from 'index';

function transform($ctx: Context, $config: Record<string, any>, $input: Dataset): Dataset {
    for (const person of $input.people.values()) {
        person.first_name = 'John';
        person.last_name = 'Doe';
        person.display_name = 'John Doe';
    }

    return $input;
}

Set a person's role based on their display name

This transformation loops over all the people in the dataset and sets their role to Teacher if their display_name starts with Mr., Mrs., or Ms..

import { Dataset, Person, Role, GenderIdentity } from 'index';

function transform($ctx: Context, $config: Record<string, any>, $input: Dataset): Dataset {
    for (const person of $input.people.values()) {
        if (person.display_name.startsWith('Mr.')) {
            person.roles = [Role.Teacher];
            person.demographics.gender = GenderIdentity.Male;
        } else if (person.display_name.startsWith('Mrs.') || person.display_name.startsWith('Ms.')) {
            person.roles = [Role.Teacher];
            person.demographics.gender = GenderIdentity.Female;
        }
    }

    return $input;
}

Things to be aware of

• first_name and last_name are different than display_name, and they will not change together.
• Deleting entities with dependents (via the entity delete() function) will delete the dependents as well. For example, deleting a person will delete all their enrollments.