Web scraping and web crawling terms often get confused. However, these terms define different processes. Let’s jump right into the explanation of web scraping vs web crawling.
Web crawling is the process of indexing website information with bots (crawlers). Bots crawl the website by going through every web page and every link inside them. Search engines use web crawling to index pages.
Web scraping means automatically extracting specific datasets from a target website. Data extraction is done by bots, also called scrapers, and is used for data comparison, ad verification, and data analysis.
When it comes to the difference between web scraping and web crawling, the short answer is that web scraping defines the process of extracting data from the web, while web crawling means finding URLs on the web.
Most data extraction projects combine both web scraping and web crawling. Target websites are crawled to discover the URLs and download HTML files. Only then the process of web scraping begins, as you scrape the data from the crawled pages.
What are the Benefits of Web Scraping?
Web scraping is so popular for a reason. It has a number of benefits, and here are the main ones:
- Data accuracy — compared to manual data extraction, web scraping has a large advantage. Scraped data is accurate because it’s collected automatically and this eliminates the risk of human error.
- Price — automatically extracting data may be cheaper than having a team of people collecting information. When choosing a ready-to-use web scraping tool, companies can also save on maintenance costs compared to building an in-house web scraper.
- Filtering — various scrapers can filter data based on your needs. For example, you can collect target data sets that are relevant to your business rather than manually looking for certain information or trying to filter relevant data from a large chunk of raw information.
What are the Benefits of Web Crawling?
Web crawling has many benefits, including:
- Fresh data — crawling websites provides snapshots of target website data in real-time. This information can help companies monitor various updates and website changes in order to compare historical data.
- Minimal interference — web crawlers work in the background and don’t interfere with website performance. This is especially relevant for busy websites that attract a lot of traffic.
- In-depth website view — web crawling can help get an accurate overview of a certain website and its structure. Once the crawler finishes its job, the scraper can extract relevant information.
Web Scraping Use Cases
Various companies turn to web data extraction to achieve their business goals. While there are plenty of web scraping use cases, here are the most popular ones:
- E-commerce — online marketplaces contain valuable data that competitors collect for market analysis and other business cases. For example, companies extract data from competitor sites about their product pricing at certain time periods and use it to build dynamic pricing strategies.
- Brand protection — cybercriminals often hide behind different brands, which may cause reputational damage to companies. Web scraping enables gathering information and monitoring brand mentions, which can help companies protect their brand.
- Research — web scraping helps collect data at a large scale in real-time. Scraped information can then be used for various research, for example, to identify trends, forecast various changes, and compare user behaviors in different periods.
Web Crawling Use Cases
Various companies can utilize a web crawler for a number of use cases. The most popular ones are:
- Indexing — search engines are the most prominent users of web crawlers. Indexing pages help search engines provide relevant search results for different queries. Web owners want their content to be found on search engines, so they often submit their web pages for crawling themselves.
- SEO optimization — crawling also helps search engines spot unique content and identify if something is plagiarized and unauthentic. This information allows for providing better search results for various queries.
- Competitor monitoring — capturing snapshots of competitor web pages allows companies to make decisions based on web data rather than guesses. Bots can help monitor when competitors add new product pages to their marketplaces or remove certain URLs.
Web Scraping vs Web Crawling: the Challenges
Despite the key differences, web scraping and web crawling bots can run into mutual challenges when collecting data. Here are the main challenges and suggestions for their solutions:
IP blocks — data collection can quickly stop if your bots get blocked from the target site. Sending too many requests from the same IP will get it banned from the site.
The solution to potential IP blocks is using reliable proxies and rotating them to avoid getting banned while gathering data.
Dynamic content — many sites use dynamic content to improve the website’s user experience. It includes lazy-loading images, infinite scroll, and similar elements. This content raises challenges for web crawlers and scrapers.
Using a headless browser with a set of proxies can help solve this challenge.
Honeypot traps — some websites place links in the HTML elements that aren’t visible to regular users but end up being crawled by a web crawler.
You can set your bots to look for certain CSS elements to avoid these traps.
Web scraping and web crawling are the terms that often get mixed up. However, these terms define different processes. Web scraping means extracting web data from various sites, while data crawling means finding URLs on target websites.
Web scraping has various benefits and covers different use cases compared to data crawling. Web scraping is used for data collection from various sites to perform research, monitor competitors, and protect a brand.
Web crawling is mainly performed by search sites. They use bots to crawl websites in order to index them and extract data that can be later provided for relevant search queries.
While the key differences between web scraping and web crawling are their processes, they still have shared challenges. For example, automated processes can stop due to IP blocks. To avoid that, scrapers and crawlers should use proxies.