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When companies start actively working with web data, simple parsing scripts quickly stop working. A small scraper can collect information from several pages, but corporate projects often involve thousands of sites and millions of pages.
In such situations, enterprise web scraping is used.
Enterprise web scraping is a large – scale system for automatic data collection from the Internet, designed for large amounts of information and continuous operation. Unlike simple parsers, such solutions are built as a full-fledged infrastructure: they manage task queues, distribute the load, handle errors, and save data in a convenient format.
The main goal of the enterprise approach is to make data collection stable, scalable, and automatic. Companies use such systems to monitor the market, analyze prices, collect news, track changes on websites, and build analytical services.
When a project is just starting, there is usually not much data. But over time, the number of sources grows. If the system is not designed to scale, it starts to work slowly or breaks down.
That is why scalable web scraping is used for large projects.
Scalable scraping means that the system can:
This is especially important for companies that regularly collect web data. As an example, e-commerce businesses can monitor the pricing of their competitors, analysis tools can monitor the news and reports, and marketing companies gather data on products and brands.
Systems that are not developed properly make the above tasks challenging and cumbersome.
In corporate systems, data collection usually consists of several stages.
First, the list of sources is determined. These can be specific website pages, product catalogs, search results, or news sections.
After that, the data collection processes are started. Scrapers open the pages, extract the necessary information and transfer it to the processing system.
In the next step, the data is cleaned and structured. This is important because information from websites often has a different format.
The data is then stored in a database or an analytical system. After that, they can be used for reports, market analysis, or building services.
In enterprise web scraping systems, this process is automated. The system can regularly check websites and update data without human intervention.
Companies monitor prices, product ranges, and changes in offers on competitors’ websites.
Scalable web scraping helps you monitor product catalogs, product availability, and price dynamics.
Data collection is used to analyze publications, brand mentions, and content activity.
Web scraping helps you regularly receive large amounts of data for reports and analytics.
Enterprise scraping is used in services that collect and combine data from multiple sources.
The system collects information without manual work and can work continuously.
You can process a large number of sites and pages simultaneously.
Information is updated regularly, which is important for analytics and monitoring.
Automatic processes reduce the risk of missing important data.
The collected data can be transferred to reports, BI systems, and internal services.
Such solutions are most often used by teams that regularly work with web data.
Marketing and analytical teams use scraping to analyze the market and monitor competitors.
E-commerce companies use scalable web scraping to track prices and product ranges.
Data teams are building data collection systems for analytics platforms and internal services.
Enterprise scraping is also used by developers who create web content analysis tools or data aggregators. When the amount of information becomes too much for manual processing, enterprise web scraping becomes a necessary tool.
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Define target URL and connect to the API with a single line of code
Edit crawl parameters and insert your custom logic using Python or JavaScript
Retrieve website data as Markdown, Text, HTML, or JSON files
fetch('https://api.webunlocker.scalehat.link/tasks/', {
method: 'POST',
headers: {'X-API-Key': 'YOUR_API_KEY'}, 'Content-Type': 'application/json'},
body: JSON.stringify({url: 'https://example.com'})
});
requests.post(
'https://api.webunlocker.scalehat.link/tasks/',
headers={'X-API-Key': 'YOUR_API_KEY'}, 'Content-Type': 'application/json'},
json={'url': 'https://example.com'}
)
curl -X POST https://api.webunlocker.scalehat.link/tasks/ \
-H "X-API-Key: $API_KEY" \
-H "Content-Type: application/json" \
-d '{"url": "https://example.com"}'
Enterprise web scraping is a large – scale system for automatically collecting data from websites, designed for large amounts of information and continuous operation.
To optimize a web scraper, task queues, query rate limiting, error handling, and data processing optimization are usually used.
Scaling of web scraping is achieved by distributing tasks between several processes or servers, which allows you to simultaneously process a large number of sites.