How to Extract Data from Hidden APIs with Make.com (Step-by-Step)

In this guide, I’ll walk you through how to extract data from hidden APIs using Make.com. This method is perfect for automating workflows and scraping data from websites where URLs don’t change dynamically.

Introduction to Hidden APIs

Hidden APIs are often overlooked yet play a crucial role in how websites serve data. These APIs are typically accessed via JavaScript and may not be immediately visible in the URL structure. They allow for dynamic content loading, meaning that the data is fetched and displayed without a full page reload. This capability is particularly useful for sites where content updates frequently or where user interactions dictate the information displayed. Understanding how to utilize these hidden APIs can unlock a wealth of data that traditional scraping methods might miss.

What Are Hidden APIs?

Hidden APIs serve as the backbone for many interactive web applications. Unlike traditional APIs that are exposed through clear endpoints, hidden APIs operate behind the scenes. They handle requests and responses that are invisible to the user but essential for the functionality of the site. For instance, when you fill out a form and submit it, the hidden API processes that request and returns the relevant data, often in a format like JSON or HTML.

Common Uses of Hidden APIs

• Data Extraction: They facilitate the collection of data for analysis or reporting.
• Dynamic Content Loading: They help in rendering content without refreshing the page, enhancing user experience.
• Real-Time Updates: They enable real-time data updates, such as stock prices or social media feeds.

Understanding the Challenge

Extracting data from hidden APIs presents unique challenges. The primary issue is that many of these APIs do not reveal themselves through standard web scraping techniques. Since the URLs don’t change with user interactions, identifying them requires a more in-depth approach. Additionally, the data returned may not always be in a straightforward format, necessitating extra steps to convert it into a usable structure.

Common Challenges Faced

• Dynamic Loading: Content is often loaded asynchronously, making it hard to capture all data at once.
• Authentication: Some APIs require authentication, complicating the extraction process.
• Changing Endpoints: API endpoints may change frequently, requiring ongoing adjustments to the extraction process.

Accessing the Network Tab

To effectively extract data from hidden APIs, the first step is accessing the browser’s network tab. This tool allows you to monitor all network requests made by the webpage. By using the network tab, you can identify which API calls are being made when you interact with the page.

Steps to Access the Network Tab

1. Open your web browser and navigate to the target website.
2. Right-click on the page and select “Inspect” or press F12 to open the Developer Tools.
3. Click on the “Network” tab to view all network activity.
4. Perform the action on the page that triggers the data fetch, such as filling out a form and clicking a button.
5. Look for requests labeled as XHR or fetch in the network log.

Identifying Web Service Calls

Once you have the network tab open, your next task is to identify the relevant web service calls. This step involves filtering through the requests to find those that fetch the data you need. It can be a bit of trial and error, but with practice, it becomes easier.

How to Identify Relevant Calls

• Filter requests by type, focusing on XHR or fetch calls.
• Look for calls that correspond to user actions, such as searching or submitting a form.
• Examine the request details, including URL, method (GET or POST), and payload to understand what data is being sent.

Setting Up Make.com for Data Extraction

After identifying the appropriate API calls, the next step is setting up Make.com for data extraction. Make.com allows for seamless integration with APIs and can automate the process of sending requests and handling responses. This setup involves creating a scenario that mimics the API requests you observed.

Steps to Configure Make.com

1. Create a new scenario in Make.com.
2. Add an HTTP module to make a request to the identified API endpoint.
3. Set the method to POST if that’s what the API requires.
4. Input the necessary headers and body parameters, mirroring what you found in the network tab.
5. Test the configuration to ensure it retrieves the expected data.

Replicating API Requests

Replicating API requests accurately is crucial for successful data extraction. This involves ensuring that all parameters, headers, and payloads match what the API expects. Data extraction can fail if any detail is overlooked.

Tips for Accurate Replication

• Review the request headers in the network tab and replicate them in Make.com.
• Ensure the body type matches the API requirements—typically either form data or JSON.
• Utilize tools like Postman to test your API requests outside of Make.com to ensure they return the expected results.

Using the Map Feature

I created an automation that leverages the map feature in Make.com, which is essential for handling complex data structures. This feature allows you to manipulate data dynamically, making it easier to extract the information you need from the API response.

When working with the API response, you’ll notice that the data can come in various formats. The map feature helps in converting this raw data into a structured format that you can work with. Instead of handling everything manually, the map feature automates the process, allowing you to focus on the logic of your automation.

Steps to Use the Map Feature

1. In your scenario, add a JSON module to parse the incoming data.
2. Use the map feature to extract specific fields from the parsed data.
3. Build the data structure required for your output by dragging and dropping elements into the map.
4. Test the mapping to ensure it returns the expected results.

By following these steps, you’ll be able to efficiently transform the data into a format that fits your needs.

Converting Data to JSON Format

Converting the data to JSON format is a crucial step in the extraction process. Many APIs return data in HTML or other formats, which aren’t suitable for further processing. JSON is a lightweight data interchange format that is easy for humans to read and write and easy for machines to parse and generate.

To convert the data to JSON, I often use a parsing module within Make.com. This module takes the raw data and converts it into a structured JSON format, making it easier to manipulate and store.

Steps for Conversion

1. Add a parse JSON module in your Make.com scenario.
2. Input the raw data you received from the API call.
3. Ensure the output is in the correct JSON format by defining the structure you need.
4. Run the scenario to validate the output.

Once the data is in JSON format, it can be easily sent to various applications like Google Sheets or Airtable for further analysis or storage.

Handling Pagination

Pagination is another important aspect of working with APIs. When the data set is large, APIs often return results in pages. This means you’ll need to implement a way to navigate through these pages to retrieve all the data.

In my automation, I handle pagination by using a repeater module. This module allows me to loop through the pages and extract data from each one until I’ve gathered all the necessary information.

Steps to Implement Pagination

1. Determine the number of pages you need to retrieve based on the total results available.
2. Add a repeater module to your scenario.
3. Set the repeater to iterate through the pages, adjusting the page parameter with each iteration.
4. Connect the output of the repeater to your data extraction module.

This approach ensures you collect all the data without missing any pages. Once all data is retrieved, it can be processed and stored as needed.

Testing the Setup

Testing your setup is crucial to ensure everything works as expected. I recommend running your automation in a controlled environment to verify each step functions correctly. This includes checking that the API calls return the correct data and that the JSON parsing is accurate.

During testing, I often focus on the following areas:

• Verify the API response format and data accuracy.
• Check if the pagination correctly retrieves all pages of data.
• Ensure the mapping feature accurately structures the data.

Steps for Effective Testing

1. Run the automation with a limited data set to minimize errors.
2. Check the logs for any errors or unexpected results.
3. Adjust the parameters and modules as necessary based on your findings.
4. Once satisfied with the initial tests, run the full automation to capture all data.

By thoroughly testing the setup, you can ensure a smooth data extraction process.

Finalizing the Data Extraction Process

Once all the data is successfully extracted, it’s time to finalize the process. This includes organizing the data in a way that makes it easy to analyze and use in your applications. I typically save the data to a Google Sheet or Airtable for easy access and manipulation.

At this stage, it’s essential to ensure that the data is clean and well-structured. This might involve removing duplicates, ensuring all fields are filled, and formatting the data appropriately.

Steps to Finalize Data Extraction

1. Choose your preferred storage solution (Google Sheets, Airtable, etc.).
2. Map the extracted data to the corresponding fields in your storage solution.
3. Run the final automation to push the data into your chosen application.
4. Review the stored data for accuracy and completeness.

This final step ensures that you have all the data you need in a format that is easy to work with, allowing you to derive insights and make informed decisions.