What is AI Web Scraping: A Comprehensive Guide
Collecting accurate and up-to-date data can be a tedious task, but you can automate the process using various tools built for web scraping. However, if you’re working with complex websites, such as media streaming platforms, a typical custom-built scraper might require a lot of effort to set up.
The more data on the web, the more ways to extract it – something as simple as a social media post or a product listing can be a valuable piece of data. A script instructed to retrieve this information can reduce manual work. While custom-built scrapers can do wonders, they can also break easily and miss crucial information. Additionally, many websites have built-in anti-scraping measures that are used to detect and block scrapers from completing their tasks. Not to mention that writing a script requires programming skills.
AI web scraping tools help avoid issues like detection because they respond to the task at hand and adjust themselves – whether it’s solving CAPTCHAs or automatically rendering JavaScript content. In this guide, you’ll learn everything you need to know about a more flexible and intelligent scraping approach – AI web scraping.
What Is Traditional Web Scraping
Traditional web scraping usually refers to automated data collection using custom-built scripts. In essence, you collect a list of URLs that you want to scrape, send a request to the target page, and your script pulls out the HTML code with all the web data. Then, if written in the script, the scraper cleans up the data to give you the information you initially asked for – it could be product listing names, prices, and whatnot. Once you have the code written, the process is quite straightforward, quick, and works as intended with most websites. However, it also comes with certain limitations.
Limitations of Traditional Web Scraping
While quite resource-intensive in the beginning, a custom web scraper can be a cost-efficient way to collect data from the web in the long run. If you’re skilled in programming or eager to learn, we recommend using Python or Node.js for writing your script – these languages are relatively simple, and have many powerful libraries for data collection and analysis. There are a lot of customizations you can do for a traditional scraper, but it’s important to understand that it will require constant attention.
- They need add-ons for dynamic content: if you build a scraper yourself, you’re the only one that’s responsible for its success. Let’s say you set the scraper to work with HTML structure. When your scraper runs into dynamic content, you’ll have to manually update and adjust the script to work again. Imagine a social media site where new posts load as you scroll – in this case, the website fetches post content via JavaScript. So, you’ll need to use a headless browser library to deal with dynamic elements. And trust me, this is harder than it sounds.
- They’re made to work with one website layout: traditional scrapers get confused when websites change their layouts without refreshing the page. This leads to breakage, and missing or inaccurate information. Even with websites that have a simple HTML structure, you’ll have to readjust your scraper manually, if the website owner changes something (even relatively small) in the structure.
- They don’t “multitask” well: website layout changes probably won’t be much of an issue if you work with only one website. But if your case requires scraping loads of data on various websites with different structures, it will become tiresome to make adjustments.
- They’re more susceptible to antiscraping technologies: have you ever had to check a box to confirm you’re not a robot? While you’re capable of doing so, a traditional scraper usually isn’t. Websites employ various antiscraping technologies – CAPTCHAs, IP blocks, honeypot traps – to prevent robots from overloading their servers with unwanted traffic. In this case, you’ll need extra software like CAPTCHA solver and proxies to bypass web scraping roadblocks. However, it’s inconvenient and can make your script break faster.
If you already encountered these or similar issues with a custom-built scraper, or want to prevent them from happening, you should try AI web scraping.
What Is AI Web Scraping
Traditional scraping has come a long way and, to this day, is the primary choice for gathering web data. However, AI web scrapers significantly improve this process – they can scrape any website without additionally running headless browsers, CAPTCHA solvers, and manually updating the scraper.
Benefits of AI Web Scraping
- Ability to handle dynamic content and adapt to structural changes: AI-based scrapers can handle both static and complex dynamic web content because they adjust to different content types, whereas traditional scrapers have to be manually reconfigured.
- Extracted data is more accurate: AI web scrapers work faster and better because they learn from previous tasks. They can filter, contextualize, and parse information intelligently, similarly to how a human would. AI scrapers will understand the context, and will be able to extract all relevant information regardless how it’s presented. The process is more efficient, and no manual input is required – the scraper adjusts itself automatically.
- They can outsmart anti-scraping technologies: AI scrapers can bypass anti-scraping measures, such as CAPTCHAs, honeypot traps (forms invisible to humans that only bots can try to fill in, which signals automated activity), or IP blocking, triggered by a suspiciously high number of requests from one address. They do so by adjusting browsing speed, mouse movements, and click patterns to imitate how a human would behave on a website. They can also choose the right proxy type, rotate them automatically, create unique browser fingerprints, and retry failed requests.
While seemingly foolproof, AI web scrapers aren’t without flaws. Usually, AI web scrapers are quite expensive, as specific features will cost extra. Also, you have less control over functionality and features – you’re stuck with what’s included in the service, and there’s no customization you can do yourself. It’s also worth mentioning that some websites (like Google) can be off limits with some pre-made tools.
Differences between Traditional and AI Web Scraping
In essence, traditional and AI scrapers are the same thing – they scrape data. However, traditional scrapers rely on predefined rules. They are conservative and do precisely what you ask them to do. AI web scrapers, on the other hand, can adapt to the task at hand, even if you didn’t adjust it – they’re more intelligent when encountering complex websites and data.
Choosing AI Web Scraping Tools
If you need an AI web scraper, there are a couple of ways you can go about this. One way is to build a basic scraper with Python or other programming language from scratch, integrate a headless browser for dynamic content handling, a natural language processing model for semantic analysis and adaptable data extraction, a machine learning model for data analysis, and then train it.
However, it’s a hassle and requires a significantly higher programming skill level. Fortunately, there’s another option – choose from multiple scraping tools already available on the market. They usually have great performance, well-maintained infrastructure, and are designed to handle large amounts of requests. Also, it’s a much better option for one-off jobs.
No-code AI Scrapers
No-code scrapers are a great choice for people without coding experience – they usually have a user-friendly interface and ready to use templates. With a no-code scraper, you visit a website, interact with the elements you want to scrape, and the scraper translates these interactions into scraping logic and structured data. That makes the process less automated, but still, much less manual work is involved.
Not all no-code scrapers are AI-based, but most have intelligent features, such as pattern recognition, automatic adjustments, and the ability to scrape dynamic websites.
Web Scraping APIs and Proxy APIs
Web scraping APIs and proxy APIs are an automatic and programmatic way to scrape the web. They’re like remote web scrapers – you send a request to the API with the URL and other parameters like language, geolocation, or device type.
They access the target website, download the data, and come back to you with the results. They handle proxies, web scraping logic, antiscraping measures, and you don’t interact with the website yourself but, instead, write a piece of code to instruct the scraper.
The key difference between scraper APIs and proxy APIs is that the first one integrates as an API, while the latter – as proxy server through which your scraping code reroutes traffic.
Scraping Browsers
A scraping browser is a tool for automating web interactions and extracting data from web sites. It uses browser engines, like Chromium that powers Chrome, to navigate, interact with, and scrape websites, handle dynamic content, and anti-scraping measures. Libraries like Puppeteer also have AI plugins, which can help you programmatically control a regular browser like Chrome or Firefox to perform sophisticated scraping tasks. An AI-powered scraping browser can mimic human actions, like clicks, scrolls, filling out forms, thus extracting data without being detected by anti-bot measures. This is especially important if you’re aiming to scrape JavaScript-heavy websites with strong anti-scraping measures.
AI-Based Platforms
AI-based scraping platforms have a lot of features that make complex scraping tasks easier to handle. These tools help you write and execute scripts, manage scale, and how the data is extracted and stored. Usually, they require a good technical understanding and the ability to write code, but less maintenance. They have ready-made functions, built-in parsers, and the ability to adapt to changes and adjust scraping scale and rules. Some AI-based platforms also have visual tools to make scraping accessible and less technically challenging.
Best Practices for AI Web Scraping
Websites don’t like being scraped. They want real humans to browse, engage, and make purchases. Scrapers, on the other hand, create unwanted traffic that can overload the servers and doesn’t bring any revenue. Nevertheless, web data collection isn’t illegal.
There are no laws that would prohibit you from scraping the web, but it’s essential to do it ethically and responsibly. Here are some tips on how to scrape ethically:
- Respect robots.txt file: in simple terms, robots.txt is a file that websites use to instruct web crawlers and scrapers on what they can and cannot do. It helps websites ensure that some parts of the website remain private.
- Respect the Terms of Service: it goes without saying, but you should adhere to the rules given by the website owner. Some ToS might forbid automated data extraction, and you should respect that.
- Scrape politely: when scraping, try to be as respectful to the website as possible – don’t overload the servers with too many requests, don’t access forbidden information, and respect the rules imposed by ToS and robots.txt files.
- Respect personal data: scraping someone’s personal information without consent violates privacy laws and raises many ethical concerns. Always comply with personal data protection laws, such as the General Data Protection Regulation (GDPR) or The California Consumer Privacy Act (CCPA). Otherwise, you can hurt your business’s reputation and face legal implications.
Conclusion
AI and machine learning enhance data scraping by handling dynamic content, recognizing complex patterns, and adapting to structural changes. Intelligent features like CAPTCHA solving, automatic proxy management, and semantic content analysis improve the accuracy, speed, and flexibility of scraping. As a result, the data is more structured, easier to understand, and requires less manual work.
