Tutorial: Building a Web Scraper that Handles Dynamic Content, Infinite Scroll, and AJAX Loading

Websites today are increasingly dynamic, with content loaded via JavaScript, infinite scrolling, and AJAX calls. Traditional scraping methods using static HTML parsing often fail to capture all the data.

This tutorial shows how to build a robust web scraper that handles dynamic content, infinite scroll, and AJAX loading, ensuring reliable extraction at scale. Grepsr implements these techniques in its pipelines to deliver high-quality, structured datasets to clients.

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1. Understanding Dynamic Content, Infinite Scroll, and AJAX

Dynamic Content

• Content rendered by JavaScript instead of static HTML
• Requires browser rendering or API inspection to extract

Infinite Scroll

• Pages load more content as the user scrolls down
• Content is not present in the initial HTML

AJAX Loading

• Content fetched asynchronously from APIs in the background
• Requires capturing network requests or rendering scripts

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2. Tools and Libraries You’ll Need

• Python 3.x: Core language for scraping
• Playwright or Selenium: Browser automation for dynamic content
• BeautifulSoup / lxml: Parsing rendered HTML
• Requests / HTTPX: API and HTTP requests
• Pandas / PyArrow: Data cleaning and storage
• Airflow / Prefect (optional): Scheduling recurring scraping tasks

Grepsr Implementation:

• Uses Playwright for dynamic content extraction
• Pipelines automate scrolling, AJAX handling, and structured data storage

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3. Step-by-Step Scraper Development

Step 1: Set Up Browser Automation

Install Playwright:

pip install playwright
playwright install

Basic Playwright setup in Python:

from playwright.sync_api import sync_playwright

with sync_playwright() as p:
    browser = p.chromium.launch(headless=True)
    page = browser.new_page()
    page.goto("https://example.com")

Grepsr Tip:
Headless browsers allow rendering JavaScript without opening a GUI, making extraction faster and scalable.

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Step 2: Handle Infinite Scroll

Dynamic pages often load additional content as you scroll:

import time

scroll_pause_time = 2
last_height = page.evaluate("document.body.scrollHeight")

while True:
    page.evaluate("window.scrollTo(0, document.body.scrollHeight);")
    time.sleep(scroll_pause_time)
    new_height = page.evaluate("document.body.scrollHeight")
    if new_height == last_height:
        break
    last_height = new_height

Grepsr Tip:
Automated scrolling captures all content without missing hidden or dynamically loaded items.

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Step 3: Capture AJAX Calls

Some websites load content via API endpoints:

1. Open browser DevTools → Network tab → Filter XHR requests
2. Identify API calls returning JSON
3. Use Requests or HTTPX to fetch JSON data directly

import requests

response = requests.get("https://example.com/api/data")
data = response.json()

Grepsr Tip:
Direct API calls are faster and more reliable than parsing rendered HTML when possible.

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Step 4: Parse Rendered Content

Once content is rendered or fetched, parse it:

from bs4 import BeautifulSoup

soup = BeautifulSoup(page.content(), "html.parser")
items = soup.find_all("div", class_="product")
for item in items:
    name = item.find("h2").text
    price = item.find("span", class_="price").text

Grepsr Tip:
Combine BeautifulSoup with lxml for fast and robust parsing.

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Step 5: Store and Structure Data

import pandas as pd

data = [{"name": name, "price": price} for item in items]
df = pd.DataFrame(data)
df.to_csv("products.csv", index=False)

Grepsr Tip:
Automated pipelines push structured data into warehouses or APIs for client delivery.

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Step 6: Handle Errors and Anti-Bot Detection

• Rotate IPs and user-agents to avoid blocking
• Implement retries and error logging
• Monitor scraper performance

Grepsr Approach:

• Pipelines detect blocks, CAPTCHAs, or layout changes and adapt automatically

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Step 7: Automate Scheduling

Recurring scraping requires orchestration:

• Airflow / Prefect: Schedule extraction pipelines
• Implement retries, logging, and alerts
• Automate delivery to clients via API or cloud storage

Grepsr Example:

• Daily extraction pipelines run automatically
• Data validated, enriched, and delivered to client dashboards without manual intervention

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4. Best Practices

1. Respect website terms of service and robots.txt
2. Use headless browsers for dynamic content
3. Optimize scraping frequency to avoid overloading servers
4. Validate and clean data before storage
5. Implement monitoring to detect changes in page layout or extraction failures

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5. Real-World Example

Scenario: A client wants to track thousands of product listings on e-commerce sites with infinite scroll and AJAX content.

Grepsr Solution:

1. Playwright handles infinite scroll and dynamic JS content
2. AJAX endpoints are captured for faster extraction
3. Scraped data is cleaned, validated, and structured
4. Delivered daily via API to client analytics dashboards

Outcome: Reliable, scalable, and automated extraction, with real-time insights for pricing and inventory decisions.

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Conclusion

Scraping dynamic websites requires careful handling of JavaScript-rendered content, infinite scroll, and AJAX. By using modern tools like Playwright, Selenium, and Requests, combined with automated pipelines, businesses can extract reliable, structured data at scale.

Grepsr pipelines implement these best practices, delivering high-quality datasets efficiently to clients without manual intervention.

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FAQs

1. How do I scrape dynamic content?
Use headless browsers like Playwright or Selenium to render JavaScript and capture page content.

2. How is infinite scroll handled?
Programmatically scroll the page, detect new content, and repeat until all content is loaded.

3. How do I capture AJAX-loaded content?
Inspect network requests and fetch JSON API endpoints directly using Requests or HTTPX.

4. How can I prevent scraper blocking?
Rotate IPs, use multiple user-agents, throttle requests, and handle CAPTCHAs carefully.

5. How does Grepsr implement dynamic scraping?
By combining browser automation, API capture, error handling, validation, and scheduling for automated, scalable extraction pipelines.