RD Station and LinkedIn Data Scraper 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 RD Station, triggered by another scenario, or executed manually (for testing purposes). In most cases, RD Station or LinkedIn Data Scraper will be your first step. To do this, click "Choose an app," find RD Station or LinkedIn Data Scraper, and select the appropriate trigger to start the scenario.

Add the RD Station Node

Select the RD Station node from the app selection panel on the right.

Configure the RD Station

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

Add the LinkedIn Data Scraper Node

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

Authenticate LinkedIn Data Scraper

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

Configure the RD Station and LinkedIn Data Scraper 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 RD Station and LinkedIn Data Scraper 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 RD Station, LinkedIn Data Scraper, 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 RD Station and LinkedIn Data Scraper integration works as expected. Depending on your setup, data should flow between RD Station and LinkedIn Data Scraper (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.