What Is Web Scraping? A Beginner’s Guide to How It Works

Key takeaways

• Web scraping is the process of automatically extracting data from a website by parsing its HTML code and storing it in a structured format, like JSON.

• Popular web scraping tools include requests and parsing libraries in Python or JavaScript, headless browsers, Scrapy, and no-code tools like Parsehub and Octoparse.

• Web scraping is legal as long as the information you’re scraping is publicly available and you’re not scraping any personal or copyrighted information.

The type of web scrapers you’ll use can range from self-built to prebuilt, browser extensions to software, and locally hosted versus cloud-hosted. Discover the mechanism of web scraping, the different types of web scrapers, popular web scraping tools and libraries, and the ethical considerations of web scraping. If you’re ready to start building expertise in data analysis and web scraping, enroll in the DeepLearning.AI Data Analytics Professional Certificate. You’ll have the opportunity to learn key concepts like ETL, large language modeling, data manipulation, and statistical analysis in as little as four months. Upon completion, you’ll earn a career certificate to add to your resume.

What is web scraping?

Web scraping is the process of automatically extracting data and other types of information from a website by collecting its HTML formatting and then storing the data in a structured format, like JSON or XML. Data collected through web scraping can help businesses make more informed decisions or help provide training data for machine learning (ML) models. For the scraping process, you’ll either use specialized tools or custom-make a script using a programming language like Python to automatically search the web and gather vast amounts of data.

Web scrapers vs. APIs

An application programming interface (API) allows two software applications to communicate and share data. Some websites, like Stack Overflow and X, provide API endpoints for data extraction; however, if the website doesn’t have APIs or provides only limited access to data, you’ll need a web scraper to obtain the data.

Since the website providers themselves maintain the APIs, it often guarantees speed, availability, and reliability. However, they may leverage tiered pricing models to fund the upkeep of the API servers, and you’ll need to pay a fee to access the API key.

Depending on the scale of your project, web scraping might be less expensive if you’re looking to build a web scraper from scratch, since all you’ll need is an internet connection and sufficient computational resources. However, web scraping might be less stable or reliable since the efficiency of the scraping process depends on the target sites. If servers are down, the site’s structure changes, or it uses anti-scraping mechanisms, you’ll likely need to use other bypassing methods.

How web scraping works

In general, web scraping involves giving the scraper the URLs of the websites you want to scrape, defining extraction criteria (what data to extract), and storing the data in a usable format.

The basic web scraping process

The steps in web scraping are as follows:

1. You identify the URLs of the websites you want to scrape and feed this information to the web scraper.

2. The scraper sends an HTTP request to the websites’ servers, similar to a browser, and the website responds with the requested data, which can be the HTML code of the page, JavaScript files, or cascading style sheets (CSS) elements.

3. The scraper will then parse the returned information and extract data based on the criteria you specified, such as prices, customer reviews, or images.

4. The scraper then converts the extracted data into a structured format, such as JSON, CSV, or Excel, or sends it directly to your database.

Types of web scrapers

The types of web scrapers depend on who built them, whether they’re applications or software, and where they run.

Self-built vs. prebuilt scrapers

If you have enough programming knowledge, you can build your own web scraper using a language like Python, avoiding licensing costs and retaining full control. However, you’ll need advanced coding knowledge to write complex programs that can handle issues like dynamic content loading and perform regular maintenance.

Prebuilt scrapers, in contrast, are already-written programs that you can download and customize as per your needs. They can help when you have less time to collect data.

Browser extensions vs. software

Browser extensions are add-on programs you can install in your browser that use the browser's abilities to extract dynamic data. Although user-friendly, they're limited by the browser's capabilities and can't handle complex functions like CAPTCHA or large-scale scraping.

Web scraping software runs on your personal computer or the cloud and offers more flexibility and advanced scraping features than browser extensions. They’re generally more suitable for large-scale, complex scraping tasks but often involve usage-based pricing.

Local vs. cloud

Local scrapers run on your desktop, which can be cheaper when starting out or for one-off projects. However, their efficiency is limited by your hardware’s computing power, internet connection, and your internet provider’s data caps. Cloud-based scrapers mitigate most of these issues since the scraping software runs on cloud servers operated by the software providers. While this frees up your computer’s resources, you’ll typically have to pay associated costs to maintain your subscription.

Do you need coding skills for web scraping?

You don’t need to have robust coding knowledge if you’re using no-code tools or software. However, having coding knowledge can be beneficial if you need to customize your scraping software, debug, or troubleshoot. If you’re planning on building a web scraper, coding knowledge is essential, ideally in a language like Python or JavaScript.

Popular web scraping tools and libraries

When building your own web scraper, it’s important to understand the various coding libraries and frameworks that facilitate and automate different aspects of the coding process. The following lists some of the libraries well-suited to creating web scraping applications:

• Request libraries: These make the HTTP calls to servers and receive the data. For example, Requests in Python, Node Fetch in JavaScript, and RestClient in Ruby.

• Parsing libraries: These allow the scrapers to extract specific HTML data. For example, BeautifulSoup in Python, Cheerio in JavaScript, and Nokogiri in Ruby.

• Headless browsers: These behave like real browsers but without a user interface, and they’re essential for scraping dynamic web content. For example, Selenium and Playwright in Python and Puppeteer in JavaScript.

• Scrapy: As an open-source framework for data extraction, Scrapy offers built-in support for robust pipelines, large-scale web crawling, and other integrations and libraries.

Alternatively, no-code tools are software programs that can extract data from the web without requiring you to write or maintain any code. These solutions can, in turn, save you time, handle increased scale, and offer advanced features such as scheduling. However, these platforms often come with associated costs like subscriptions and data storage. Some popular web scraping platforms include Parsehub, Octoparse, ScraperAPI, Oxylabs, Mozenda, and Decodo.

Common use cases of web scraping

Web scraping fuels data-driven decision-making, business intelligence, and automation across a range of areas:

• Price intelligence: Businesses scrape e-commerce websites to extract product and pricing information and devise a more competitive pricing strategy. This enables them to formulate a dynamic pricing structure based on market conditions and optimize revenue.

• Market research: Companies use web scraping to better understand market trends like seasonal products, competitor pricing, and customer sentiment, powering business intelligence and research and development.

• Lead generation: By scraping clients’ websites, businesses can obtain their contact details and other relevant information in much less time than when done manually. This allows sales and marketing teams to reach the right customers for their products or services.

• Real estate: Using a web scraper to gather data from real estate websites can help real estate agents and companies make informed decisions by understanding regional pricing trends, which listing sites have more sales, and where the highest vacancy rates are.

• AI model training: Large language models (LLMs) require large amounts of data, like news articles, research papers, and public forums, to inform the model’s intelligence. Web scraping helps compile a diverse data set of high-quality data for model training.

Read more: What Is Data-Driven Decision-Making (DDDM)? And How to Get Started

Challenges of web scraping

Web scraping, despite its many productive uses, comes with certain security challenges. Cybercriminals can;

• Use bots to scrape websites to find vulnerabilities.

• Launch distributed denial of service (DDoS) attacks, or extract sensitive information about users, leading to data breaches.

• Scrape a website to create a fraudulent site that tricks users through a phishing email, stealing their personal information in the process for identity theft.

To prevent these breaches, companies often use encryption, like;

• CAPTCHA, rate limiting, and bot detection tools. While this may safeguard against cyberattacks, it can also block your legitimate web scrapers.

• Program your scraping bot to simulate more humanlike behavior and employ IP proxies to bypass these safeguards

• Follow the rules set in the robots.txt file of the website, which usually outlines which website parts are safe to scrape and which aren’t.

Is web scraping legal? Ethical implications to consider

Web scraping is legal if the information you’re scraping from a website is publicly available, and you’re not scraping any personal or copyrighted information. However, if you ignore the directives and rate limits in the website’s robots.txt file, it may be considered unethical. Ensure your scraping programs are responsible, avoid overloading the website’s servers with back-to-back requests, and follow the restrictions on what you can and can’t scrape in the robots.txt file.

How to start with web scraping

If you want to build your own web scraper, start by learning a programming language. Python and JavaScript are easier to learn and have compatible environments for building web scrapers. Consider taking an online course, like the Python for Everybody Specialization, to gain an understanding of foundational programming concepts. Start by learning how to make HTTP requests in the language, parse HTML, handle exceptions, and export data.

Additionally, you’ll need to understand HTML and CSS to better grasp the website structure. The Meta Front-End Developer Professional Certificate can be a useful place to start learning about how these elements make up a website. Once you have the basics down, you can start learning more advanced concepts like using Playwright or Selenium, headless browsers, firewalls, and other anti-scraping techniques.

Once you’ve built your scraper bot, you may practice by scraping websites like Quotes to Scrape and Books to Scrape. Since every website is different, you can experiment with building different types of web scrapers to hone your problem-solving skills.

Find the tools you need to keep moving forward

Subscribe to Career Chat on LinkedIn to get timely updates on popular skills, tools, and certifications in software development. Build or refresh your programming, data analysis, or web scraping skills with our other free resources:

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• Watch on YouTube: Speed Up R Web Scraping: GitHub Copilot Tutorial

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