Web scraping has become essential for individuals and businesses to extract valuable insights from online sources. There are numerous techniques and tools available for data collection. Each web scraping technique has its strengths and limitations. Therefore, choosing a web scraping approach that is appropriate for your data collection project is challenging.
In this article, we’ll explain some popular web scraping techniques, including manual, automated, and hybrid. We’ll also discuss the benefits and limitations of each method and provide practical tips on choosing the right approach for your data collection project.
Please note that regardless of the web scraping technique used, you must use these scraping techniques responsibly and respect the terms of service of the website you want to scrape.
Automated web scraping techniques
Automated web scraping techniques involve utilizing software to collect web data from online sources automatically. They are more efficient and scalable for large-scale web scraping tasks.
1.Web Scraping Libraries
Web scraping libraries are software packages that provide pre-built functions and tools for web scraping tasks (Figure 1). These libraries simplify the process of navigating web pages, parsing HTML data, and locating elements to extract. Here are some examples of popular web scraping libraries:
- Scrapy: Provides a framework for building web scrapers and crawlers. It is a good choice for complex web scraping tasks that require logging in or dealing with cookies.
- Selenium: It automates web interactions and collects data from dynamic sites. Selenium is a good choice for scraping websites that require user interaction, such as clicking buttons, filling out forms, and scrolling the page.
Figure 1: The chart shows the popularity of programming languages between 2013-2022
2. Web Scraping Tools
A web scraping tool is a software or program that automatically gathers data from web sources. Depending on several factors, such as your organization’s unique requirements, resources, and technical expertise, you can use an in-house or outsourced web scraper (Figure 2).
In-house web scrapers allow users to customize the web crawler based on their specific data collection needs. However, building an in-house web scraping tool requires technical expertise and resources, including time and maintenance efforts.
Figure 2: Roadmap for choosing the right solution for data collection projects
The table below presents the best web scraping tools designed to automate the scraping process. For an in-depth comparison, read our research on this subject.
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In-house web scraper:
- Customization: Can be customized to meet specific scraping needs and business requirements.
- Control: Provides full control over the data pipeline process.
- Cost-saving: can be more cost-effective in the long run than using a pre-built scraping bot.
- Technical expertise: Familiarity with web scraping libraries such as Beautiful Soup, Scrapy, or Selenium.
- Maintenance: Require development and maintenance effort.
Outsourced web scraper:
- Technical expertise: Do not require technical knowledge.
- Time savings: These tools are maintained by a third-party provider.
- Reduced risk: Some web scrapers offer unblocking technologies to bypass anti-scraping techniques such as CAPTCHAs.
- Cost: It may be more expensive to outsource the development of web scraping infrastructure.
Bright Data’s web scraping IDE provides users an intuitive visual interface for building their web scrapers. Here are some of the features of the Web Scraper IDE:
- Built-in debug tools: Allows developers to troubleshoot and resolve issues that may arise during the data collection process.
- Built-in Proxy & Unblocking: Offers advanced proxy management features, built-in fingerprinting and CAPTCHA solving to bypass anti-scraping measures.
3. Web Scraping APIs
Web scraping APIs enable developers to access and extract relevant data from websites. Websites can provide web scraping APIs, such as Twitter API, Amazon API, and Facebook API. However, some websites may not offer APIs for the targeted data, requiring the use of a web scraping service to collect web data. API may be more cost-effective than web scraping:
- If the desired data is available through an API
- The amount of data required is within the limits of the API
Smartproxy’s web scraping API allows businesses and individuals to extract data from web sources using API calls. It includes proxy features that allow users to scrape data from websites without being blocked.
4. Optical Character Recognition (OCR)
OCR software reads text elements in non-text formats, such as PDFs or images. It captures web data elements from sites using a screenshot or another method to extract the desired data from the recognized text. However, there are some limitations you must be aware of when extracting data using OCR.
- It may have difficulty recognizing small or unusual fonts.
- The accuracy of OCR relies on the input image quality. For instance, poor image quality, such as blur, can make it challenging or impossible for OCR software to recognize text accurately.
- It may struggle to recognize text data in columns, tables, or other complex layouts.
5. Headless Browsers
Headless browsers such as PhantomJS, Puppeteer, or Selenium enable users to collect web data in a headless mode, meaning that it runs without a graphical user interface.
Headless browsers can be a powerful tool for scraping dynamic and interactive websites that employ client-side or server-side scripting. Web crawlers can access and extract data that may not be visible in the HTML code using headless browsers.
It interacts with dynamic page elements like buttons and drop-down menus. The following are the general steps for collecting data with a headless browser:
6. HTML Parsing
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: There are several factors to consider when selecting a parser, such as the programming language being 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: 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.
7. DOM Parsing
DOM parsing allows you to parse HTML or XML documents into their corresponding Document Object Model (DOM) representation. DOM Parser is part of the W3C standard that provides methods to navigate the DOM tree and extract desired information from it, such as text or attributes.
Manual web scraping 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 error-prone, so it is important to use them only when it is necessary for data collection projects.
8. Manual Navigation
It is the process of manually navigating through a website and collecting web 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 preferable.
- Screen Capturing: It is the process of 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 collect data from web sources. This approach is practical when automated web scraping techniques cannot extract the required data completely.
Assume you extracted data using an automated web scraping technique like an API call. When reviewing your scraped data, you discovered missing or incorrect information. In this case, you can use manual web scraping to fill in the missing or inaccurate data elements. Using hybrid web scraping techniques can help verify the accuracy and completeness of the scraped data.
- 7 Web Scraping Best Practices You Must Be Aware of
- Web Scraping Tools: Data-driven Benchmarking
- Top 7 Python Web Scraping Libraries & Tools
- Web Scraping APIs: How-To, Capabilities & Top 10 Tools
For guidance to choose the right tool, check out data-driven list of web scrapers, and reach out to us:
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