Main Methods of Web Data Collection
Collecting data from the Internet is an indispensable tool for business, science and technology. The volume of information has increased tenfold over the past 5 years. The methods of its extraction have become more difficult due to stricter safety rules and legal regulations.з
APIs vs Scraping
By 2025, the market for data collection tools will be fragmented: some solutions are suitable for point—based tasks, while others are suitable for large-scale projects. The choice depends not only on technical capabilities, but also on legal constraints, budget, and the level of expertise of the team. Let's look at which technologies are dominant and in which cases they should be used.
Using libraries like Scrapy or Selenium gives you full control over the data collection process. For example, Scrapy allows you to set up asynchronous requests, which is critical for parsing large e-commerce platforms with millions of product cards. How to collect data from websites, this approach requires deep programming knowledge and time to maintain the code — each update to the site structure can "break" the parser. Selenium, which emulates user actions in the browser, is indispensable for bypassing anti-bot systems, but consumes significant resources. In 2025, it is often combined with AI modules for automatic CAPTCHA recognition, which complicates the setup.
Tools like Octoparse reduce the time needed to launch projects. A marketer without coding skills can set up price collection from competitive websites in an hour. But simplicity has a downside: limited customization and dependence on platform updates. For example, ParseHub, despite its support for dynamic sites, does not always cope with resources where content is generated via WebSocket.
By 2025, No-code solutions have added AI features such as automatic page structure detection. How to gather online data, for complex scenarios (for example, data parsing with authorization), they are still inferior to their programmable counterparts.
Cloud platforms like Bright Data solve two key problems: infrastructure and legality. Their proxy networks and built-in anti-blocking tools allow them to collect data from different regions without risking their IP reputation.
AI scraping, as in the case of Diffbot, automatically adapts to changes in the structure of sites, reducing the time spent on parser maintenance. Neural networks also analyze behavioral patterns to mimic "human" actions, such as random delays between clicks. But the introduction of such technologies requires not only a budget, but also expertise. Training models on specific data (for example, custom captcha recognition) can take months. In addition, AI solutions consume more computing resources, which increases operating costs.
Legal & Ethical Aspects in 2025
In 2025, the laws governing data collection in the West and in Russia have become stricter. Technology is developing faster than laws. This creates problems and increases risks for the business.
The Computer Fraud and Abuse Act (CFAA) has been updated in the USA. He treats unauthorized access to information as a criminal offense. This applies to public and private information. In 2025, a California court ruled it illegal to scrape LinkedIn profiles without the official consent of the social network's management.
Ethical standards remain a high priority. Collecting information can damage the company's reputation. Aggressive parsing of news sites with a high frequency of requests slows down the resource. This violates the postulates set forth in F.A.I.R. Data (Findable, Accessible, Interoperable, Reusable). In 2025, an ethical audit is a standard event in large corporations.
Tips for reducing legal risks:
Working through the API. Platforms allow collection on their own terms.
Using a proxy.
Coordination with the management. The email request has long served as a legal shield. In 2025, 30% of startups will use this tool.
Monitoring robots.txt Marketplaces prohibit the parsing of price information. Ignoring this requirement can lead to lawsuits.
Use Cases for Businesses
Automated collection and analysis technologies allow businesses to respond to changes and predict trend changes. Marketing strategies cannot be implemented without analyzing the digital footprints of the audience. Federal and regional networks use social network parsing to identify trends. Algorithms track hashtags, frequency of mentions, and popularity in different regions. Companies adapt their advertising campaigns by offering personalized conditions to potential customers. Brandwatch uses AI to predict audience interests.
Real-time price changes have become the norm in e-commerce. Large retailers use cloud scraping services to monitor and analyze changes in the market. This allows you to instantly adjust your business strategy by offering discounts or bonuses to your customers.
In 2025, innovative plug-ins combining parsing and machine learning were announced. Algorithms predict the impact of external factors on demand, and automatically generate recommendations.
In 2025, companies are actively using generative AI to automatically respond to reviews.
Fintech startups use scraping of news feeds and social networks. The approach allows you to more accurately predict the volatility of cryptocurrencies.
The information collection market is changing rapidly. Specialists need to be aware of innovations in this area. It is better to perform one-time tasks using no-code plugins. AI-enabled cloud services are ideal for large-scale projects.
NB: Please note that the product is intended for automating tests on your own websites and sites you have legal access to.
