Web scraping is not the only method for collecting data from websites. Various other methods (e.g., LLMs) are available, and each technique has trade-offs.
See the best web scraping techniques, the benefits and limitations of each method, and practical tips on choosing the right approach for your data collection project:
The “build vs. buy” decision: sourcing your scraping solution
1. Building an in-house web scraper
This approach involves using programming languages and libraries to create custom web scrapers tailored precisely to your needs. You have full ownership and control over the entire data pipeline, from the initial request to the final structured output.
Pros:
- Customization and control: You can build the web scraping tool to meet your exact specifications, handling unique website structures, complex logic, and specific data formatting requirements. You control the entire data pipeline and are not limited by a third party’s features.
- Cost-effectiveness at scale: While there’s an upfront investment in development time, running an in-house scraper can be significantly cheaper in the long run for very large, continuous projects, as you aren’t paying per request or a high monthly subscription fee.
- Data security: The data you scrape is processed on your own infrastructure, giving you full control over privacy and security, which can be critical for sensitive information.
Cons:
- Technical expertise: Building a robust scraper requires strong programming skills and familiarity with web scraping libraries such as Beautiful Soup, Scrapy (for Python), or Puppeteer (for JavaScript/Node.js).
- High upfront investment: The initial development and setup demand a significant investment of time and resources before you can collect any data.
- Continuous maintenance burden: Websites frequently change their layouts. This means your in-house team is responsible for updating the scraper, managing proxies, handling IP blocks, and solving CAPTCHA, which requires ongoing effort.
Tools for building your own scraper:
- Web Scraping libraries and frameworks:
- Beautiful Soup: For parsing static HTML and XML documents (Python).
- Scrapy: A full-featured framework for complex, large-scale crawling projects (Python).
- Cheerio: A fast, lightweight parser for static sites (JavaScript).
- Headless Browsers for dynamic sites:
- Selenium: The industry standard for browser automation, simulating user actions like clicks and scrolls.
- Puppeteer: A modern library for controlling headless Chrome/Chromium browsers (JavaScript).
A headless browser is a complete web browser that operates invisibly in the background, possessing all the capabilities of a standard browser like Chrome or Firefox, but without a graphical window on your screen. This makes it a powerful tool for scraping modern, dynamic, and interactive websites.
You can even program it to perform actions a real person would, such as scrolling down to load more content, filling out a login form, or selecting an option from a dropdown menu.
This ability to execute JavaScript and simulate user interactions is what makes headless browsers indispensable for scraping modern web pages.
2. Using a third-party web scraping service (Outsourced)
This approach involves paying a third-party company that has already built and maintains a large-scale web scraping infrastructure. You typically access their services through a Web Scraping API.
They simplify the process immensely. Instead of writing code to handle browsers, proxies, and blocks, you just send a single API call with the URL you want to scrape. The service then performs all the heavy lifting in the background and returns the clean, structured data to you, typically in JSON format.
Pros:
- Ease of use: This is the fastest way to get data. You can go from zero to scraping in minutes without needing to be a scraping expert. The service provider handles all the technical complexity.
- Managed infrastructure: You don’t have to worry about the most difficult parts of scraping. The provider manages a huge pool of proxies, rotates IP addresses, uses headless browsers for JavaScript rendering, and scales the infrastructure for you.
- Bypassing anti-scraping measures: These services are experts at overcoming defenses like CAPTCHA, browser fingerprinting, and IP blocks, a task that is a major challenge for in-house scrapers.
Cons:
- Higher operational costs: For large-scale use, subscription fees or pay-per-request models can be more expensive than running your own scraper. You are paying for convenience and managed infrastructure.
- Less flexibility: You are limited to the features and data formats offered by the provider. If you have a very unique requirement, the service might not be able to accommodate it.
- Data dependency: Your entire data collection pipeline is dependent on a third-party provider. If their service goes down or changes, your operations are directly affected.
Generative AI and Large Language Models (LLMs)
Here’s how generative AI models work alongside traditional data scraping techniques:
3. LLMs as a development accelerator
In 2024, the adoption of Generative AI and Large Language Models (LLMs), such as OpenAI’s GPT-4, Google’s Gemini, and Anthropic’s Claude, grew substantially, marking a new era in data scraping. These models have evolved from simple text generators into powerful coding assistants.
For web scraping, this means you can now use tools like ChatGPT or other AI coding assistants to guide you in writing code, effectively lowering the barrier to entry and accelerating development for even experienced programmers.
Using an LLM as a coding partner involves a conversational, iterative process. Instead of memorizing the exact syntax for a library, you describe your goal in plain English, and the AI translates it into functional code.
4. LLMs as a parsing engine
Sample HTML code can be input to LLMs. Then, LLMs can identify specific sections (e.g., prices, product descriptions) from that data. This technique is best suited for scenarios where traditional parsing is challenging, such as scraping sites with frequently changing layouts, extracting data from unstructured paragraphs, or for rapid prototyping where speed of development is more important than the cost per page.
While highly accurate, making an API call to a powerful LLM for every page you parse is more expensive than running a local parsing library like Beautiful Soup.
5. LLMs as autonomous agents
The scraping operation doesn’t need to be a single-step solution. AI agents can run multi-step processes and make decisions. For example, tools like LangChain combine web scraping with LLMs, enabling users to ask for extraction of specific information, like all product reviews mentioning “durability” on an e-commerce page.g “durability” on an e-commerce page.
Sponsored
Oxylabs provides OxyCopilot, an AI-powered custom parser builder that enables users to extract specific, relevant data (such as product names, prices, etc.) by directing the API through prompts. For instance, we used it to retrieve only four specific fields from a given URL.
Fundamental extraction methods: Parsing and OCR
6. Decoding the web: Parsing HTML and the DOM
HTML parsing is another technique used to extract data from HTML code automatically. Here are some steps to collect web data through HTML parsing:
- Inspecting the HTML code of the target page involves using a browser’s developer tools to view the HTML code of the web page you intend to scrape. This enables users to understand the structure of the HTML code and locate the specific elements they want to extract, such as text, images, or links.
- Choosing a parser involves several key factors, including the programming language used and the complexity of the website’s HTML structure. The parser you choose must be compatible with the programming language you use for web scraping. Here is a list of some popular parsers for different programming languages:
- Beautiful Soup and lxml for Python
- Jsoup for Java
- HtmlAgilityPack for C#
- Parsing HTML: The Process of reading and interpreting the HTML code of the target web page to extract specific data elements.
- Extracting data: Collect the specific data elements using the parser.
Sometimes, the data you need isn’t actually text in the HTML code; it’s locked inside an image, a scanned PDF, or a screenshot. For these cases, you need Optical Character Recognition (OCR).
OCR is a technology that recognizes and extracts text from non-text formats. The process involves:
- Capturing an image of the data on the target site (e.g., by taking a screenshot).
- Using OCR software to read the text elements within that image.
- Extracting the desired data from the recognized text.
However, there are limitations to consider:
- Font and layout challenges: OCR can struggle with small, stylized, or unusual fonts. It may also have difficulty recognizing text arranged in complex layouts, such as columns or tables.
- Dependency on image quality: The accuracy of OCR is highly dependent on the quality of the input image. Blurry, low-resolution, or distorted images can make it challenging or impossible for the software to accurately recognize text.
8. DOM Parsing
DOM parsing allows you to parse HTML or XML documents into their corresponding Document Object Model (DOM) representation. The DOM Parser is part of the W3C standard, providing methods to navigate the DOM tree and extract desired information, such as text or attributes.
- How it works: You can use methods like XPath, a language for selecting nodes in an XML or HTML document, to pinpoint the exact elements you want to scrape. This is the same query language used in the Google Sheets IMPORTXML function.
Simple & Manual techniques
Manual web scraping may be justifiable for small-scale or one-time web scraping projects where automated scraping techniques are not practical. However, manual scraping techniques are time-consuming and prone to errors, so it is essential to use them only when necessary for data collection projects.
9. Scraping with Google Sheets
For those who want to automate the data collection process without writing any code, Google Sheets is a powerful tool for the job. Using Google’s built-in functions, you can pull specific data directly from a website’s HTML into your spreadsheet.
This technique is suitable for small, simple scraping tasks, pulling data from web pages with a clear and stable HTML structure, and for users who are not programmers.
- How it works: The primary function used is =IMPORTXML(“URL”, “XPath_query”). You provide the target web page’s URL and then an XPath query to pinpoint the exact piece of data you want to extract. For example, you could pull the title of a webpage, a specific table, or a list of links.
- Limitations: This method is not suitable for large-scale scraping, as it can be slow and is limited by Google’s quotas. It also cannot handle websites that rely heavily on JavaScript to load their content.
10. Manual navigation
It is the process of manually navigating through a website and collecting data along the way. If the desired data is dispersed across multiple pages or is not easily accessible through automated scraping techniques, manual navigation may be a preferable option.
- Screen capturing: This process involves taking screenshots of data on the target website and manually entering the captured data into a document, such as a spreadsheet.
- Data entry: This involves manually entering data from the target website into a file
Hybrid web scraping techniques
Hybrid web scraping combines automated and manual web scraping techniques to extract data from web sources. This approach is practical when automated web scraping techniques are unable to extract the required data completely.
When is a hybrid approach necessary?
You should consider a hybrid approach when your project involves:
- Data validation and quality assurance: When the accuracy of the scraped data is critical, a final human review is needed to verify its completeness and correctness.
- Inconsistent website layouts: When a script works for most pages but fails on a few that have a unique or outdated design.
- Complex anti-scraping measures: For websites where a script can handle most tasks but gets stuck on a particularly difficult CAPTCHA or a login that requires two-factor authentication (2FA).
- Data requiring human judgment: When extracting data that is subjective or requires context, such as determining the sentiment of a product review or interpreting ambiguous text.
Industry Analyst
Gulbahar Karatas
Industry Analyst
Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security.
