LinkedIn Data Scraper and Trainerize Integration

Add the First Step

Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a LinkedIn Data Scraper, triggered by another scenario, or executed manually (for testing purposes). In most cases, LinkedIn Data Scraper or Trainerize will be your first step. To do this, click "Choose an app," find LinkedIn Data Scraper or Trainerize, and select the appropriate trigger to start the scenario.

Add the LinkedIn Data Scraper Node

Select the LinkedIn Data Scraper node from the app selection panel on the right.

Configure the LinkedIn Data Scraper

Click on the LinkedIn Data Scraper node to configure it. You can modify the LinkedIn Data Scraper URL and choose between DEV and PROD versions. You can also copy it for use in further automations.

Add the Trainerize Node

Next, click the plus (+) icon on the LinkedIn Data Scraper node, select Trainerize from the list of available apps, and choose the action you need from the list of nodes within Trainerize.

Authenticate Trainerize

Now, click the Trainerize node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Trainerize settings. Authentication allows you to use Trainerize through Latenode.

Configure the LinkedIn Data Scraper and Trainerize Nodes

Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.

Set Up the LinkedIn Data Scraper and Trainerize Integration

Use various Latenode nodes to transform data and enhance your integration:

• Branching: Create multiple branches within the scenario to handle complex logic.
• Merging: Combine different node branches into one, passing data through it.
• Plug n Play Nodes: Use nodes that don’t require account credentials.
• Ask AI: Use the GPT-powered option to add AI capabilities to any node.
• Wait: Set waiting times, either for intervals or until specific dates.
• Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
• Iteration: Process arrays of data when needed.
• Code: Write custom code or ask our AI assistant to do it for you.

Save and Activate the Scenario

After configuring LinkedIn Data Scraper, Trainerize, and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.

Test the Scenario

Run the scenario by clicking “Run once” and triggering an event to check if the LinkedIn Data Scraper and Trainerize integration works as expected. Depending on your setup, data should flow between LinkedIn Data Scraper and Trainerize (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.