Data acquisition Scrapers Tutorials Python

What is Web Scraping & How to Scrape Data from a Website?

Iveta Liupševičė

Last updated by Dovydas Vėsa

2026-03-13

14 min read

The internet is full of invaluable public data that can assist companies in achieving their goals. In this article, we explain web scraping and how to effectively get website data for your business through all the most popular methods. Plus, for your convenience, we also have this topic covered in a video format:

What is Web Scraping?

Web scraping, also known as internet scraping, web data extraction, or website scraping, refers to the automated process of collecting publicly available data from a website. Instead of gathering data manually, automated web scraping tools or specialized scraping APIs send requests to a specific URL, retrieve the page’s HTML (Hypertext Markup Language), parse the relevant elements, and convert the extracted information into structured data formats such as CSV or JSON.

You can use web scraping to extract data from various websites, depending on the goals of the scraping project. For instance, e-commerce businesses leverage web scraping to compare prices, monitor competitors, and refine their strategies by collecting public pricing data, customer reviews, product descriptions, and more. Meanwhile, cybersecurity companies employ web scraping to monitor threats across the web, while structured data is also increasingly used to support AI and machine learning models that rely on large-scale web data extraction.

How Web Scrapers Work

Automated scraping typically requires two core components: a web crawler and a web scraper. Web crawling (using a spider or bot) discovers and navigates URLs across a website, while the web scraping process extracts specific data from each page.

At a high-level view, the automated scraping workflow follows these core steps:

Input target URL(s)
Send an HTTP request to the server
Retrieve the HTML response
Parse the page’s DOM (Document Object Model)
Extract relevant elements
Clean and structure the data
Export it to a file or database

For dynamic websites that rely heavily on JavaScript, advanced scrapers also render the page in a headless browser before parsing the content to gather all the content that is not present in basic HTML page data.

How Scrapers Extract Data from HTML Code

Scrapers analyze the HTML structure of a web page and parse its DOM to locate specific elements such as headings, tables, links, or product cards. Using CSS selectors or XPath to identify patterns in tags, classes, and attributes, the parser isolates only the relevant data while ignoring unnecessary parts.

Using XPath and CSS Selectors to Locate Data

As mentioned earlier, to precisely locate elements within HTML, scrapers commonly use CSS selectors or XPath expressions. CSS selectors are typically simpler and work well for selecting elements by class, ID, or hierarchy. XPath provides more advanced navigation within the document structure, making it useful when extracting deeply nested data.

Exporting Scraped Data to JSON and CSV Formats

Once data is extracted, it is converted into structured formats such as JSON, CSV, or XLSX files. For larger projects, scraped data may also be stored in databases to support analytics, reporting, or integration into automated workflows, such as analytics applications or AI agents.

Web Scraping with Python: Why It's the Top Choice

Python is the most widely used programming language for web scraping due to its readability, large ecosystem, and extensive library support. Popular Python libraries such as Beautiful Soup, Scrapy, Requests, and Selenium simplify tasks like sending HTTP requests, parsing HTML, and handling dynamic JavaScript content.

While other programming languages, such as JavaScript (Node.js), Ruby, and PHP, can also be used for scraping techniques, Python is the preferred choice for most developers due to its simplicity and strong community support.

The Process of Web Scraping: Step-by-Step Guide

Before starting any web data extraction, it's crucial to explain the basic web scraping process. Here’s a step-by-step guide on how to extract data:

1. Identify target websites

Depending on your objective or your chosen scraping techniques, the initial step in any scraping project involves identifying the specific web pages from which you intend to gather relevant information.

For a quick web scraping demonstration, let’s crawl and scrape this e-commerce website demo. To keep it simple, we’ll showcase the steps for Python, which you can download from the official website. Once you have it ready, open your computer’s terminal and install the required Python libraries for this project using pip:

python -m pip install requests beautifulsoup4 pandas

Depending on your setup, you may want to use the python3 keyword:

python3 -m pip install requests beautifulsoup4 pandas

The requests library will send web requests to the target server to retrieve HTML documents, while Beautiful Soup will help you extract data from each scraped HTML file.

2. Collect target page URLs

To be more efficient when scraping data, collecting specific URLs can help you save your resources. You'll collect only the needed data without vast amounts of irrelevant information from multiple pages.

Let’s say your goal is to extract data from each product listing. To do this, you need to consider pagination and collect all the product URLs from each search page. If you scroll down the first page to the bottom, you can see there are a total of 94 pages:

Since each page URL has a page parameter, for example, https://sandbox.oxylabs.io/products?page=1, you can easily generate URLs for all 94 pages using Python. As the purpose of this guide is to showcase the steps, let’s just scrape the first five pages:

# === url_crawler.py === #
import requests
from bs4 import BeautifulSoup


urls = []

for page in range(1,6):
    print(f"Scraping page {page}.")
    response = requests.get(f"https://sandbox.oxylabs.io/products?page={page}")

Next, convert raw HTML to a Beautiful Soup object for parsing:

    html_doc = BeautifulSoup(response.text, "html.parser")

Then, open up your browser’s Developer Tools by right-clicking and selecting Inspect. This way, you can view the HTML code of a page and determine the CSS selectors or XPath expressions for extracting the public data you need. You can see that each product card is in a <div> tag with a class set to “product-card”, while the link of each product is inside the <a> tag’s href attribute:

Using this information, you can form the URL extraction logic and save all product URLs into a .txt file, as shown in the Python code below:

# === url_crawler.py === #
import requests
from bs4 import BeautifulSoup


urls = []

for page in range(1,6):
    print(f"Scraping page {page}.")
    response = requests.get(f"https://sandbox.oxylabs.io/products?page={page}")
    html_doc = BeautifulSoup(response.text, "html.parser")

    for product in html_doc.find_all("div", class_="product-card"):
        link = product.select_one("a")
        urls.append("https://sandbox.oxylabs.io" + link.get("href"))

with open("urls.txt", "w") as f:
    for url in urls:
        f.write(url + "\n")
print("URLs saved to a file.")

3. Make requests to get the HTML of the page

This critical step is where the essence of the entire project unfolds. By making requests, you retrieve the HTML of the desired pages containing all the necessary website elements.

In a new Python file, let’s build a scraper that reads the urls.txt file and makes a request for each URL:

# === scraper.py === #
import requests
import pandas as pd
from bs4 import BeautifulSoup


with open("urls.txt", "r") as f:
    urls = f.readlines()

product_data = []
for url in urls:
    print(f"Scraping: {url.strip()}")
    response = requests.get(url.strip())

4. Navigate & extract information from the HTML

Following the acquisition of HTML code, the scraper navigates through it to extract specific unstructured data, presenting it in the structured format specified by the user. Here’s how you can achieve this in Python:

    html_doc = BeautifulSoup(response.text, "html.parser")

    product_data.append({
        "title": html_doc.select_one("h2").get_text(),
        "price": html_doc.select_one(".price").get_text(),
        "developer": html_doc.select_one(".developer").get_text().replace("Developer: ", ""),
        "link": url.strip()
    })

5. Store scraped data

This is the final step of the whole web scraping process. The extracted data needs to be stored in CSV, JSON format, or in any database for further usage.

For this short guide, let’s use the pandas library to save the parsed product data to a CSV file:

df = pd.DataFrame(product_data)
df.to_csv("product_data.csv", index=False)

In the end, you should have a basic code that looks like this:

# === scraper.py === #
import requests
import pandas as pd
from bs4 import BeautifulSoup


with open("urls.txt", "r") as f:
    urls = f.readlines()

product_data = []
for url in urls:
    print(f"Scraping: {url.strip()}")
    response = requests.get(url.strip())
    html_doc = BeautifulSoup(response.text, "html.parser")

    product_data.append({
        "title": html_doc.select_one("h2").get_text(),
        "price": html_doc.select_one(".price").get_text(),
        "developer": html_doc.select_one(".developer").get_text().replace("Developer: ", ""),
        "link": url.strip()
    })

df = pd.DataFrame(product_data)
df.to_csv("product_data.csv", index=False)

You can open the CSV file in a program that supports the CSV format. For example, here’s a snippet of the saved file in Google Sheets:

Web Scraping Tools and Software

When it comes to scraping data from a website, there are multiple approaches you can take, each with its own benefits and limitations. Choosing the right solution depends on your specific goals, resources, and technical skills, including coding knowledge.

Browser Extensions and No-Code Web Scraping

For beginners or those who want a fast, easy way to gather web data without heavy coding, no-code and low-code options are ideal:

Manual copy and paste: The most straightforward method involves manually copying data from a website, though it's time-consuming for large-scale data extraction.
Browser developer tools: Most modern browsers include built-in tools, such as the "Inspect" feature, that allow you to view a site's source code and extract elements directly. Learn more about how to scrape images from a website using browser tools.
Browser extensions: Some extensions for Chrome and Firefox can automate simple scraping tasks by identifying and capturing patterns on web pages, or offer simple point-and-click tools that any non-developer can use to select specific fields of required data.
RSS feeds: Some websites provide structured data through RSS feeds, which is a simple way to gather updated content without the need for coding.
Web scraping services: Many platforms offer data scraping as a service, automating the process with little or no technical input required.
Data mining software: Several software suites offer integrated scraping alongside data analytics tools, providing a low-code approach to managing both collection and analysis, often used in data science applications.

Headless Browsers: Selenium and Puppeteer for Dynamic Sites

Headless browsers, such as Selenium or Puppeteer, allow developers to automate browser interactions using code. These tools render JavaScript and load dynamic page elements, making them useful when scraping websites that rely heavily on client-side rendering and save a lot of resources in the process.

Selenium supports multiple programming languages and browsers, while Puppeteer is primarily used with Node.js and provides tight integration with Chrome and Chromium-based browsers.

Automated Web Scraping Techniques

For those requiring more robust and scalable solutions, advanced methods involving coding can provide greater control and flexibility:

Scrapy: Python’s Scrapy framework is a robust option for handling larger-scale or more complicated web data. It’s well-suited for experienced users who need advanced functionality.
Web crawling frameworks: For large-scale scraping, frameworks like Apache Nutch can automate data collection across vast numbers of web pages, although this requires significant technical expertise.
Scheduled scraping: Preparing a well-structured scraping scheduler may go without saying for scraping veterans, but it’s a tried-and-true practice for recurring public data collection jobs, especially useful for analytics.
Proxy rotation: When working with sites that incorporate aggressive rate limiting or IP bans, it’s highly advisable to include a proxy rotation in your requests to make each of them look unique.

Using APIs as an Alternative to Web Scraping

Many websites provide their own APIs that allow developers to access structured data directly instead of extracting it from HTML pages. APIs typically return information in formats such as JSON or XML, making them easier to integrate into applications.

On the other hand, APIs often come packaged with rate limits, authentication requirements, or restrictions on the available public data. When an API does not provide the needed information, web scraping remains a practical alternative. You can learn more about how to crawl a website without getting blocked while scraping data.

Web Data Extraction: DIY vs. Outsourcing

Building an in-house scraper provides maximum control and customization but requires development resources, infrastructure management, and ongoing maintenance. Outsourcing to specialized scraping services reduces potential overhead and allows the teams to focus on analyzing the collected data instead of maintaining scraping systems.

For complete control over your web scraping projects, building a custom in-house scraper may be the best option. This method offers:

Customization and control: You can tailor your data scraping solution to your exact needs, ensuring it fits specific use cases and scales with your projects.
Tech stack: Python and JavaScript are the most common programming languages for web scraping. Libraries like Beautiful Soup and Scrapy can simplify the process.
Proxies: For any projects based on web scraping, it’s necessary to use and maintain your proxy solution. Using proxies is crucial for gathering vast amounts of valuable data without being blocked by targeted websites, as they help rotate IP addresses. For starters, you can get a free proxy list from a reputable provider.

However, this approach requires a dedicated development team and significant resources to maintain and manage the scraper effectively.

Best Web Scraper Tools for Your Project

When selecting web scraping tools for your case, the best option depends on factors such as technical expertise, project size, scalability requirements, and budget.

Some tools focus on simple data extraction through graphical interfaces, while others provide enterprise-grade scraping infrastructure designed to handle millions of requests.

Scraper APIs: These pre-built tools allow you to collect data without extensive coding, often through easy-to-use interfaces and documentation. Despite a few drawbacks associated with third-party solutions, such as limited customization, cost considerations, and the challenge of finding a reliable provider, there are decent options available that can cater to your specific needs. For instance, you may explore our Web Scraper API to address your requirements.

Additionally, many third-party web scraper tools and scraping services allow developers and analysts to collect data without building the entire infrastructure themselves. These tools often provide built-in features such as proxy management, scheduling, cloud execution, and automatic parsing.

Plus, proxy scrapers integrate seamlessly with other platforms to enhance functionality. For example, you can explore proxy integration with ParseHub. For more advanced users, Scrapy proxy integration is another valuable option.

Datasets as an Alternative to Scraping

If you’re wondering how to scrape data from a website or whether web scraping is a suitable solution for you, datasets, even if they're not a type of web scraper, can serve as an excellent alternative for acquiring the required valuable data. You don’t need to manage the whole web scraping process with datasets because you get ready-to-use data in a preferred format. However, it's important to note that maintaining the relevance of datasets can pose challenges, particularly in dynamic fields where information is constantly changing, especially with the advent of large language models being trained on vast datasets.

Choosing the right web data extraction solution or datasets always depends on your needs. Before making any decision, you should think of what you expect from it now and, of course, in the future.

Choosing the right solution depends on your needs

Web Scraping, Privacy, and Legal Considerations

The web scraping legality is a frequently discussed topic, and it’s especially important for businesses. Therefore, there are some things you need to know before starting web data scraping:

Even if you're gathering publicly available data, ensure that you’re not breaching laws that may apply to such data, e.g., downloading copyright-protected data.
Avoid logging in to websites to get the required information because by doing that, you must accept Terms of Service (ToS) (or other legal agreement), and scraping content may be forbidden.
Data for personal usage should also be collected cautiously, according to the websites’ policy.

Before scraping data of any kind, we advise you to seek legal consultation to ensure you’re not violating any laws.

Additionally, websites often publish a robots.txt file outlining crawling and scraping guidelines for automated bots. While robots.txt is not legally binding in many jurisdictions, respecting such directives is generally considered good practice in ethics and part of responsible web scraping.

Finally, scraping data that falls under personally identifiable information (PII) raises additional privacy concerns, particularly under regulations such as the European Union’s GDPR. Businesses engaging in web scraping should ensure compliance with applicable data protection and copyright laws, as well as website terms of service.

Web Scraping vs. Web Crawling

Simply put, web crawling means navigating the web to index content, while web scraping is focused on extracting public data from a target website. Both scraping and crawling complement each other in the overall public data gathering process, often done sequentially – one following the other. Learn more about web scraping vs. web crawling by reading our in-depth article about this topic.

Web scraping vs. web crawling

Web Scraping Challenges: Overcoming Common Obstacles

Web scraping poses various challenges that can make the web data extraction process complex. The primary hurdles include the risk of being blocked by target websites, issues related to scalability, unique HTML site structures, and the ongoing need for infrastructure maintenance.

How Proxies Help You Scrape Without Getting Blocked

Proxies play an important role in automated web scraping because they help distribute requests across multiple IP addresses. By rotating IPs, scrapers can avoid rate limiting or IP blocking imposed by target websites.

Residential proxies are the best bet for the most protected sites, as they make your traffic look like regular internet users, but are usually more expensive, while datacenter proxies are the budget-friendly option with high speeds when IP authenticity is not the primary requirement. Overall, rotating proxy networks are commonly used to maintain stable scraping operations when collecting large amounts of data.

Overcoming Cloudflare, CAPTCHAs, and Anti-Bot Systems

Websites commonly employ strategies to regulate incoming traffic, including measures such as CAPTCHAs, rate limiting, IP blocking, browser fingerprinting, or protection services such as Cloudflare. Using proxies from reputable providers and managing user agents may help you overcome this challenge, especially if you use a suitable proxy type for your project, such as residential proxies.

Scraping Data at Scale

Scaling web scraping projects often includes additional challenges such as infrastructure management, bandwidth requirements, and scheduling large numbers of requests. Distributed scraping systems or cloud-based scraping tools can help manage these workloads efficiently.

Website structure changes

Websites are constantly improving their user experience, meaning their design, features, or layout might change occasionally. These changes can impact a web scraping process, meaning your web scraping tool needs to be constantly updated.

We suggest checking our blog post dedicated to main web scraping challenges and how to overcome them.

Web Scraping Use Cases

Web scraping is widely used across industries to gather, analyze, and utilize publicly available information from websites. If you automate public web data collection, your organization can gather more insights that support market analysis, business intelligence, and future strategies. Below are some of the most common applications of web scraping.

1. Web Scraping for E-Commerce Price Monitoring

Retailers and e-commerce companies use web scraping to monitor competitor pricing and track product availability across multiple online stores. Collecting publicly available pricing data from shopping websites allows you to analyze market trends and adjust your pricing strategies accordingly. Scraping also helps brands enforce Minimum Advertised Price (MAP) policies more easily for better competitive standings.

2. Alternative Data for Finance and Investment

Financial analysts often scrape data from financial news websites, stock market platforms, and public company reports. This information can help investors analyze market sentiment, monitor stock performance, and identify emerging opportunities. Hedge funds and investment firms frequently rely on scraped alternative data to support forecasting and trading strategies.

3. Real Estate Data Collection

Real estate professionals use web scraping to gather property listings and rental prices from the marketplaces. Data like this helps agencies analyze vacancy rates, re-evaluate property values, or calculate better rental yields.

4. Lead Generation and Email Marketing

Businesses often use web scraping to collect publicly available company and contact information from directories and widely known professional platforms. Such data can help sales teams build prospect lists and identify potential customers. When used responsibly, scraping add a lot of support for targeted outreach campaigns.

5. SEO and Competitive Analysis

SEO professionals scrape search engine results pages (SERPs) to track keyword rankings and analyze competitor strategies. Scraping search results allows marketers to identify content gaps, monitor backlinks, and study how competing websites structure their pages. These insights help improve search visibility and refine SEO campaigns.

Marketers scrape publicly available posts and comments from social media platforms to analyze customer sentiment or monitor brand mentions. This can help you examine large volumes of user-generated content, while large businesses can understand how audiences see their brand mentions or even combat negative content. While with a lot of caveat, some even go as far as as using Publicly available posts on Twitter/X or Facebook as their data sources.

7. Market Research Automation with Web Scraping

Market research teams use web scraping to collect large amounts of publicly available data about consumer behavior, product demand, and competitor activity. Collecting product listings, reviews, and pricing data from multiple sources can identify trends and show new market opportunities.

8. News Monitoring and Content Aggregation

Web scraping can also be used to monitor news websites and aggregate articles on the same topics from multiple sources. Scraping news data can track headlines, publication dates, and article metadata to stay informed about industry developments or helps businesses quickly identify relevant events and trends for investment decision-making.

Data Extraction Tools: From No-Code to Custom Scrapers

While there is a wide range of data extraction tools, from simple no-code tools to fully custom scraping systems, the right choice for you depends on on these main factors:

Team’s technical expertise
Target data volume
Future scalability requirements

In fact, most solutions can be easily separated in a few different categories.

Solution	Technical Skill	Best For
Manual copy-paste	None	Small, one-time data collection
Browser extensions / No-code tools	Low	Quick scraping tasks for non-developers
Web scraping APIs	Low–Medium	Collecting various amounts of structured data with minimal setup and maintenance
Custom Python scrapers	Medium–High	Flexible data extraction and automation
Enterprise scraping solutions	High	Large-scale, automated data pipelines

Each method has trade-offs between flexibility, reliability, and maintenance effort. For large-scale projects or business-critical applications, companies often use professional web scraper tools or all-in-one APIs, such as Oxylabs Web Scraper API, that handle infrastructure, scaling, and anti-bot challenges.

Web Scraping for AI and Machine Learning

Modern AI and machine learning systems depend heavily on large datasets, and web scraping is one of the most efficient ways to gather that data. All information from publicly available websites can build datasets used for data science and even Large Language Model (LLM) training or fine-tuning.

Scraped data can support a wide range of AI applications. For example, text datasets are used for natural language processing (NLP), image collections support computer vision models, and structured datasets enable predictive analytics and automated intelligence systems. Because machine learning models rely on high-quality input data, organizations must ensure their scraped datasets are accurate, consistent, and ethically collected.

Automating Web Data Collection for AI Training

Training modern AI systems requires massive volumes of both structured and unstructured data. Automated web data collection allows organizations to continuously gather and update datasets used for machine learning models. Combining large-scale scraping pipelines with data validation processes, can build more reliable training datasets for AI development or implement RAG workflows to upgrade existing LLMs.

Wrapping It Up: Key Takeaways on Scraping Data & Automated Data Extraction

There isn’t any hesitation that web scraping is a crucial process for businesses that make data-driven decisions. Whether companies build their own web scraping tools or use third-party solutions, implementing business automations with data scraping in their daily tasks is a definite improvement and a step forward.

If you want to perform web scraping right away, try our Web Scraper API for free with up to 5K results, or check various tutorials on our blog for more information.

Frequently asked questions

What is data scraping and how does it work?

Data scraping refers to extracting information from websites using automated tools. A scraper sends a request to a webpage, retrieves the HTML, and parses the content to locate specific elements. The extracted data is then converted into structured formats such as CSV or JSON for analysis or integration into databases.

What is web scraping used for?

Web scraping is used for extracting data from a website for various purposes, such as data analysis, research, monitoring, and content aggregation.

What is an example of web scraping?

A great example of web scraping is extracting product prices and reviews from e-commerce websites to analyze market trends and make informed business decisions.

Can you get banned for web scraping?

Yes, web scraping can lead to being banned or blocked by websites, as it may violate terms of service and policies. This is the reason why seeking legal consultation to ensure you’re not violating any laws before engaging in web scraping activities is important.

Is it legal to scrape data from websites?

The legality of web scraping varies depending on factors such as the website’s terms of service, the type of data being scraped, and local regulations. Generally, scraping publicly accessible data is legal, but scraping private, copyrighted, or sensitive information without permission could violate laws like the Computer Fraud and Abuse Act (CFAA) in the U.S. It’s crucial to review the terms of service of the website and ensure compliance with relevant legal guidelines in your jurisdiction.

How do I extract raw data from a website?

To extract raw data from a website, there are several approaches based on your technical knowledge. A common method is to use browser developer tools, such as the "Inspect" feature, to view the website’s source code and manually extract the data. For more automated solutions, you can use web scraping tools or programming libraries like Python’s Beautiful Soup or Scrapy to scrape the data programmatically. Additionally, third-party services and APIs provide a streamlined way to extract raw data with minimal coding.

Can Excel scrape data from a website?

Yes, Excel can be used to scrape data from websites by utilizing the Web Query tool. This feature allows you to pull data from a web page directly into your Excel spreadsheet. By entering the website's URL into Web Query, Excel can retrieve and structure the data for you to work with. It's a straightforward way to extract data from a website without needing advanced programming skills.

How do I scrape data from a website in Chrome?

To scrape data from a website using Chrome, you can leverage the browser’s built-in developer tools or install browser extensions. One option is to right-click on the webpage, select "Inspect," and use the developer tools to locate and extract specific elements of the page. Alternatively, Chrome extensions like Data Miner or Web Scraper allow you to automate the scraping process by selecting data directly from the webpage and exporting it in a usable format. For smaller datasets, manually copying and pasting data from the website is another option.

What programming language is best for web scraping?

Python is the most popular programming language for web scraping thanks to libraries like Beautiful Soup, Scrapy, and Requests. It mostly praised for simple syntax and large ecosystem, making it ideal for both beginners and advanced developers. JavaScript, particularly with Node.js, is also widely used for scraping dynamic websites that rely heavily on client-side rendering.

What is the difference between web scraping and web crawling?

Web crawling is the process of discovering and indexing web pages by following links across websites, while web scraping focuses on extracting specific information from those pages once they are identified. In many workflows, they’re used in tandem to first find relevant URLs and then collect the structured data from the page content.

Is web scraping ethical?

Web scraping can be ethical when done responsibly and in compliance with a website’s terms of service. Many websites specify crawling rules in a robots.txt file that indicates which pages automated tools should avoid. Responsible scraping also includes respecting rate limits and avoiding the collection of sensitive or restricted information on the website.

Forget about complex web scraping processes

Choose Oxylabs' advanced web intelligence collection solutions to gather real-time public data hassle-free.

About the author

Iveta Liupševičė

Head of Content & Research

Iveta Liupševičė is a Head of Content & Research at Oxylabs. Growing up as a writer and a challenge seeker, she decided to welcome herself to the tech-side, and instantly became interested in this field. When she is not at work, you'll probably find her just chillin' while listening to her favorite music or playing board games with friends.

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All information on Oxylabs Blog is provided on an "as is" basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website's terms of service or receive a scraping license.