How to connect Microsoft SQL Server and LinkedIn Data Scraper
Create a New Scenario to Connect Microsoft SQL Server and LinkedIn Data Scraper
In the workspace, click the “Create New Scenario” button.

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

Add the Microsoft SQL Server Node
Select the Microsoft SQL Server node from the app selection panel on the right.


Microsoft SQL Server

Configure the Microsoft SQL Server
Click on the Microsoft SQL Server node to configure it. You can modify the Microsoft SQL Server 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 Microsoft SQL Server 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.


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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 Microsoft SQL Server 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 Microsoft SQL Server 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.

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Trigger on Webhook
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Save and Activate the Scenario
After configuring Microsoft SQL Server, 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 Microsoft SQL Server and LinkedIn Data Scraper integration works as expected. Depending on your setup, data should flow between Microsoft SQL Server and LinkedIn Data Scraper (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Microsoft SQL Server and LinkedIn Data Scraper
LinkedIn Data Scraper + Microsoft SQL Server + Slack: Scrapes LinkedIn for potential leads based on defined criteria using 'Search People'. The scraped data, including profile details and contact information, is then stored in a Microsoft SQL Server database. Upon successful storage of a new lead, a notification is sent to the sales team via Slack, alerting them about the new potential lead and providing necessary information for follow-up.
LinkedIn Data Scraper + Microsoft SQL Server + HubSpot: Scrapes LinkedIn for leads using 'Get Profile Data' based on specific search criteria. This data is stored in a SQL database. Then, the workflow creates or updates a contact in HubSpot with the data from SQL, ensuring lead information is captured in HubSpot for sales and marketing efforts.
Microsoft SQL Server and LinkedIn Data Scraper integration alternatives

About Microsoft SQL Server
Use Microsoft SQL Server in Latenode to automate database tasks. Directly query, update, or insert data in response to triggers. Sync SQL data with other apps; simplify data pipelines for reporting and analytics. Build automated workflows without complex coding to manage databases efficiently and scale operations.
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About LinkedIn Data Scraper
Need LinkedIn data for leads or market insights? Automate scraping profiles and company info inside Latenode workflows. Extract data, enrich it with AI, then push it to your CRM or database. Latenode's visual editor and affordable pricing make data-driven outreach scalable and cost-effective.
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FAQ Microsoft SQL Server and LinkedIn Data Scraper
How can I connect my Microsoft SQL Server account to LinkedIn Data Scraper using Latenode?
To connect your Microsoft SQL Server account to LinkedIn Data Scraper on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Microsoft SQL Server and click on "Connect".
- Authenticate your Microsoft SQL Server and LinkedIn Data Scraper accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I update my SQL database with new LinkedIn leads?
Yes, easily! Latenode allows you to automatically update your Microsoft SQL Server database with leads scraped from LinkedIn, streamlining lead management with no-code.
What types of tasks can I perform by integrating Microsoft SQL Server with LinkedIn Data Scraper?
Integrating Microsoft SQL Server with LinkedIn Data Scraper allows you to perform various tasks, including:
- Automatically adding new LinkedIn leads to your Microsoft SQL Server database.
- Enriching existing Microsoft SQL Server data with up-to-date LinkedIn profile information.
- Triggering database updates based on LinkedIn profile changes.
- Creating custom reports combining data from both Microsoft SQL Server and LinkedIn.
- Building lead qualification workflows using AI and data from both sources.
How do I handle large datasets when using Microsoft SQL Server?
Latenode's architecture efficiently handles large Microsoft SQL Server datasets, allowing seamless processing and transformation at scale.
Are there any limitations to the Microsoft SQL Server and LinkedIn Data Scraper integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- LinkedIn Data Scraper's capabilities are subject to LinkedIn's terms of service.
- Initial setup might require some understanding of Microsoft SQL Server database schemas.
- Complex data transformations may benefit from custom JavaScript code blocks.