What is a Scraping Bot and How To Build One

For systematic data collection from websites, a web scraping bot is used. This is a program that automatically extracts the necessary information from pages. Such software is essential in cases where the volume of data is too large for manual processing or when regular updates are required – for example, for price monitoring, review analysis, or tracking positions in search engine results.

A web scraping bot allows automation of tasks such as: accessing a website, retrieving the content of a page, extracting the required fragments, and saving them in the needed format. It is a standard tool in e-commerce, SEO, marketing, and analytics — wherever speed and accuracy of data processing are critical.

Scraping Bot: Definition

A scraper bot is a software agent that automatically extracts content from web pages for further processing. It can be part of a corporate system, run as a standalone script, or be deployed through a cloud platform. Its main purpose is to collect large-scale structured data available in open access.

To better understand the concept, let’s look at the classification of tools used as scraper bots.

By access method to content:

• Browser-based (Puppeteer, ParseHub) — launched inside a real or headless browser, works with dynamic content created using JavaScript or AJAX.
• Cloud-based (Apify, Hexomatic) — deployed on server infrastructure, providing scalability, proxy rotation, and automation.
• Hybrid (Browse AI, Zyte Smart Browser) — combine both models: use a browser for page rendering and the cloud for large-scale task execution.

By adaptability to website structure:

• Highly specialized (Indeed Scraper, WebAutomation, LinkedIn Profile Scraper in Phantombuster) — designed strictly for one site or template and breaks easily when layout changes.
• Configurable/universal (Webscraper.io, Bardeen) — work by template (CSS/XPath), can be reused on other sites without rewriting code.

By purpose and architecture:

• Scenario-based — for example, a web scraping bot in Python or JavaScript. Such solutions are tailored to a specific task or website.
• Frameworks/platforms — such as Apify or Scrapy, which provide scalable solutions, manage proxies, sessions, and logic for bypassing protection.

Read also: Best Web Scraping Tools in 2025.

Where Are Scraping Bots Used?

Scraping bots are applied across various industries and tasks where speed, scalability, and structured information are critical.

• Price Monitoring. Scraping bots automatically collect data on the cost of goods and services from competitor websites and marketplaces. This allows businesses to quickly adjust pricing policies and create competitive offers.
• Marketing Analytics. For market research, scrapers extract reviews, descriptions, ratings, product ranges, and other characteristics. Based on this information, businesses can identify market trends, analyze brand positioning, and build promotion strategies.
• Lead Generation. Bots collect contacts, company names, service types, and other data from business directories, classifieds, industry resources, and bulletin boards. The gathered information is then used to build client databases and for email marketing campaigns.
• Content Aggregation. Scraping is used for collecting news, articles, reviews, and other texts from multiple external sources. This approach is widely adopted by aggregators, information services, and analytics platforms.
• SEO Monitoring. Scrapers track website positions in search engine results, gather information about backlinks, indexed pages, competitor activity, and other SEO metrics. This is essential for auditing and optimization.
• Change Detection on Websites. Scraping bots capture updates to web content — for example, new terms appearing, text changes, new document uploads, or section removals.

Each of these directions requires a specific level of data extraction depth and protection bypass. Therefore, web scraping bots are adapted to the task — from simple HTTP scripts to full-scale browser-based solutions with proxy support and anti-detection features.

How Do Web Scraping Bots Work?

Web scraper bots operate according to a step-by-step scenario, where each stage corresponds to a specific technical action. Despite differences in libraries and programming languages, the basic logic is almost always the same.

Below is a more detailed step-by-step description with Python examples.

1. Getting the HTML Code of a Page

At the first stage, a web scraping bot initiates an HTTP request to the target URL and retrieves the HTML document. It’s important to set the correct User-Agent header to imitate the behavior of a regular browser.

import requests
headers = {'User-Agent': 'Mozilla/5.0'}
url = 'https://books.toscrape.com/'
response = requests.get(url, headers=headers)
html = response.text

Here, the bot connects to the site and receives the raw HTML code of the page, as if it were opened in a browser.

2. Parsing the HTML Document Structure

To analyze the content, the HTML must be parsed — converted into a structure that is easier to work with. For this, libraries such as BeautifulSoup or lxml are typically used.

from bs4 import BeautifulSoup
soup = BeautifulSoup(html, 'lxml')
print(soup.prettify()[:1000]) # Display the first 1000 characters of formatted HTML

Now, the HTML can be viewed as a tag tree, making it easy to extract the necessary elements.

3. Locating Required Elements

Next, the web scraping bot identifies the fragments that need to be extracted: product names, prices, images, links, and more. Usually, CSS selectors or XPath are used.

books = soup.select('.product_pod h3 a')
for book in books:
 print(book['title'])

This code finds all book titles and outputs their names.

4. Extracting and Normalizing Data

At this stage, the web scraping bot cleans and structures the data: removes unnecessary symbols, formats text, extracts attributes (for example, href or src), and compiles everything into a unified table.

data = []
for book in books:
 title = book['title']
 link = 'https://books.toscrape.com/' + book['href']
 data.append({'Title': title, 'Link': link})

The data is transformed into a list of dictionaries, which is convenient for further analysis.

5. Storing the Information

After extraction, the data is saved in the required format — CSV, JSON, Excel, a database, or transferred via API.

import pandas as pd
df = pd.DataFrame(data)
df.to_csv('books.csv', index=False)

The collected info sets can then be easily analyzed in Excel or uploaded into a CRM.

6. Crawling Through Other Pages

If the required data is spread across multiple pages, the scraper bot implements crawling: it follows links and repeats the process.

next_page = soup.select_one('li.next a')
if next_page:
 next_url = 'https://books.toscrape.com/catalogue/' + next_page['href']
 print('Next page:', next_url)

When working with websites where the content loads dynamically (via JavaScript), browser engines such as Selenium or Playwright are used. They allow the bot to interact with the DOM, wait for the required elements to appear, and perform actions — for example, clicking buttons or entering data into forms.

DOM (Document Object Model) is the structure of a web page formed by the browser from HTML code. It represents a tree where each element — a header, block, or image — is a separate node that can be manipulated programmatically.

Challenges of Using Bots for Web Scraping

Despite the efficiency of scraping, when interacting with real websites, technical and legal obstacles often arise.

Anti-Bot Protection

To prevent automated access, websites implement different systems:

• CAPTCHA — text input checks and confirmation like “I’m not a robot”;
• reCAPTCHA v2/v3 — behavior analysis and probability assessment of whether the user is human;
• JavaScript challenges — mandatory execution of scripts before loading content.

It is recommended to check out material that describes in detail how ReCaptcha bypassing works and which tools are best suited for specific tasks.

IP Address Blocking

When scraping is accompanied by a high frequency of requests from a single source, the server may:

• temporarily limit the connection;
• blacklist the IP;
• substitute page content.

To handle such technical restrictions, platforms use rotating proxies, traffic distribution across multiple IPs, and request throttling with configured delays.

Dynamic Content Loading

Some resources load data using JavaScript after the initial HTML has already been delivered, or based on user actions such as scrolling.

In such cases, browser engines are required — for example:

• Selenium;
• Playwright;
• Puppeteer.

These allow interaction with the DOM in real time: waiting for elements to appear, scrolling pages, executing scripts, and extracting data from an already rendered structure.

Changes in Page Structure

Website developers may change:

• CSS classes of elements;
• HTML layout;
• or API request logic.

Such updates can render previous parsing logic inoperative or cause extraction errors.

To maintain stability, developers implement flexible extraction schemes, fallback algorithms, reliable selectors (e.g., XPath), and regularly test or update their parsers.

Legal Restrictions

Automated data collection may conflict with a website’s terms of service. Violating these rules poses particular risks in cases of commercial use or redistribution of collected data.

Before starting any scraping activity, it is important to review the service’s terms. If an official API is available, its use is the preferred and safer option.

Are Web Scraping Bots Legal?

The legality of using scraping bots depends on jurisdiction, website policies, and the method of data extraction. Three key aspects must be considered:

• Ethical restrictions. Before launching a scraper, it is necessary to confirm that the target website does not explicitly prohibit automated data collection — this is usually indicated in robots.txt or in the terms of service (ToS).
• Protection mechanisms. Many platforms employ anti-bot defenses: IP blocking, behavioral analysis, CAPTCHAs, and dynamic content loading.
• Legal risks. In certain countries, web scraping may violate laws on personal data protection, intellectual property rights, or trade secrets.

A detailed breakdown of the legal side can be found in the article: Is Web Scraping Legal?

How to Build a Web Scraping Bot?

Creating a scraping bot starts with analyzing the task. It is important to clearly understand what data needs to be extracted, from where, and how frequently.

Python is the most popular language for web scraping due to its ready-to-use libraries, concise syntax, and convenience for working with data. Therefore, let’s consider the general process using Python as an example.

Commonly used libraries:

• requests — for sending HTTP requests;
• BeautifulSoup or lxml — for parsing HTML;
• Selenium or Playwright — for dynamic websites;
• pandas — for structuring and saving data.

A finished solution can be implemented as a CLI tool or as a cloud-based service.

Essential components include:

1. Configuration: list of URLs, crawl frequency, DOM structure.
2. Error handling: retries, logging, timeouts.
3. Proxy support, sessions, and user-agent rotation — especially critical for high-intensity workloads.
4. Result storage: CSV, JSON, SQL, or via API integration.

The process of how to build a web scraping bot is explained in detail in this article.

Conclusion

A scraping bot as a solution for automated data collection allows quick access to information from external sources, scalable monitoring, and real-time analytics processes. It is important to comply with platform restrictions, properly distribute the workload, and consider the legal aspects of working with data.

We offer a wide range of proxies for web scraping. Our selection includes IPv4, IPv6, ISP, residential, and mobile solutions.

For large-scale scraping of simple websites, IPv4 is sufficient. If stability and high speed are required, use ISP proxies. For stable performance under geolocation restrictions and platform technical limits, residential or mobile proxies are recommended. The latter provides maximum anonymity and resilience against ReCaptcha by using real mobile operator IPs.

FAQ

What is the difference between a scraping bot and a regular parser?

A parser processes already loaded HTML, while a scraping bot independently loads pages, manages sessions, repeats user actions, and automates the entire cycle.

Do you need proxies for web scraping?

Yes. They help distribute requests across different IP addresses, which improves scalability, enables data collection from multiple sites in parallel, and ensures stable operation within platform-imposed technical restrictions.

What practices increase the efficiency of scraping?

It is recommended to use IP rotation, delays between requests, proper User-Agent settings, and session management to reduce detection risks.

Which programming languages are best for web scraping?

The most popular is Python with libraries such as requests, BeautifulSoup, Scrapy, Selenium. Node.js (Puppeteer) and Java (HtmlUnit) are also commonly used.