In computing, the scraping refers to the process of automatically extracting data online, whether from a website, document, or database. This data can then be analyzed, reused, or stored for various purposes.
What is the difference between web scraping and data scraping?
The term scraping is often used as a synonym for web scraping, but there is an important distinction.
- 🟢 Web scraping : it focuses on extracting data from websites. For example, collecting prices or information about products online. It is a specific type of scraping, limited to the web.
- 🟢 Data scraping or data scraping: broader in scope, it encompasses the extraction of data from sources other than the web, such as APIs, PDF documents, CSV files, and databases.
In summary, web scraping is a specific branch of data scraping.
What are the practical uses of web scraping?
Scraping has multiple uses, in France as elsewhere, and affects various fields.
- 🔥 Competitive intelligence : monitor the prices and content of product listings among competitors such as on Amazon. In this case, we refer to web scraping on Amazon.
- 🔥 Market analysis and academic research : collect useful data for studies, academic articles, or company reports.
- 🔥 Lead generation : retrieve contact details such as a user's email address from professional directories or social networks such as LinkedIn. This applies to web scraping on LinkedIn.
- 🔥 Content aggregation : automatically gather press articles or blog posts to create an information platform.
What are the different web scraping techniques and tools?
There are several methods and tools for web scraping.
The methods include:
- ✅ Manual scraping : copy and paste data from a web page. It's simple, but it takes time and remains inconvenient.
- ✅ Automated scraping :
- Programming : use of languages such as Python (BeautifulSoup or Scrapy) or Node.js (Puppeteer). These libraries enable the processing of large databases and the analysis of information from numerous web pages.
- No-code/low-code software : these are solutions that allow you to perform scraping without having to code, as with Bright Data.
For tools, there are:
- ✔ Code libraries like Scrapy or BeautifulSoup for Python : BeautifulSoup to extract precise data and Scrapy to manage multiple websites.
- ✔ Frameworks such as Scrapy, which is a comprehensive tool for automating queries and populating databases.
- ✔ Visual tools as Octoparse. It is very useful for analyzing website content without advanced skills.
🎯 Another important point to remember about scraping in computing is that it presents some limitations.
Scraping can generally be implemented easily. However, it is important to note that some websites check and block bots. You must therefore adapt your program or go through proxies (io networks) to continue data extraction.
For example, Google limits the number of automatic queries. Similarly, some websites specify in their terms of use that automatic collection is not permitted.
Is web scraping legal?
The legality of web scraping depends on a few points:
- ➡ Terms of use for the websites.
- ➡ The type of data and its intended use.
- ➡ The legal framework of the country where the website is based and that of the person performing the scraping.
👉 In short, the web scraping is no longer limited to extracting data. It is becoming a strategic lever for anticipating trends, fueling innovation, and automating decision-making.
💬 So the question is no longer “should we do scraping?”, but “how can we use it intelligently and legally?”. Have you ever tried scraping? web scraping ?
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