In simple terms, web scraping is the process of extracting data from web pages in a (usually) structured format. This data is used for storing, analyzing, and repurposing. On the other hand, web crawling is focused on finding and indexing URLs and links, in most cases, for search engines and aggregators.
Although these two terms are used interchangeably, they are two individual processes that can be used independently or together depending on the nature of your project.
Different Goals, Different Outputs
To better understand the differences between these two processes (scraping and crawling), let’s focus on their ultimate goal:
- The main keyword in web scraping is extraction. When you build a web scraper, you want to pull specific information from a set of URLs. The output of a scraper is a formatted file or a database with all the extracted data.
- The most important keyword in web crawling is discovery. When you build a web crawler, you might know the main domain, or you might not. Either way, your goal is to discover all relevant links and URLs within a website or category to build a list of them. The output of a crawler (or spider) is a list of URLs – in some cases, organized according to some criteria.
- Another difference between the two is their process. Web crawlers can, and often do, download website information, but this is done so it can be sorted and compared to other URLs. They also gather information from the entire page. Web scrapers need to download the whole HTML file as well, but they filter this information and extract only the data points you need – like pricing, names, titles, metadata, etc.
However, as you’ve probably figured out, what happens when you want to scrape data from a specific domain but there are too many URLs for you to collect by hand, or you are just not sure which URLs to focus on?
This is the reason why these two words are so closely related. In most projects, you’ll need to build a crawler to find all the URLs, and then build a scraper to parse and extract data from them.
In the end, these are the two parts that compose the data-gathering process at scale.
Scrapers and Crawlers Face Similar Challenges
No matter which approaches you use, both scrapers and crawlers need to be able to access URLs and their content at scale, so they end up facing the same set of challenges:
- Anti-scraping and crawling policies and techniques – servers have several methods to block robots and spiders from accessing their data. Without the proper systems, your IP will be blocked from accessing the target website ever again – even if you’re not using a bot.
- Scalability and speed – one of the main reasons we use bots for scraping and crawling instead of doing it manually is because of scalability and speed. However, that’s also a problem. The number of requests, and the speed at which these are sent to the server, are metrics that servers can use to detect and block robots. Handling this limitation to achieve the best balance requires a lot of experience and resources.
- Bigger the website, harder to access – although scraping and crawling small, niche sites are quite simple, the more complex and data-rich websites use robust blocks to avoid getting scraped. There are also roadblocks like dynamic content, which make more traditional scrapers/crawlers ineffective.
Pro Tip: Use ScraperAPI to Avoid Roadblocks
ScraperAPI is a simple, powerful solution that uses machine learning, years of statistical analysis, and huge browser farms to prevent your scraping bots from getting blocked.
By just sending your requests through ScraperAPI servers, it will rotate IP addresses after every request, handle any CAPTCHAs that your scraper encounters, and choose the right IP and headers combination to ensure a 200 successful status code.
You can also add extra functionalities by adding parameters to the URL to render dynamic content or change the geolocation of your requests – thus being able to access geo-specific data.
Learn more
- Build a web scraper and crawler using Scrapy – a Python library designed to build fast and reliable spiders.
- Understand the difference between web scraping and data mining.
- Discover the most common applications for web scraping in business and technology.
- More frequently asked questions about web scraping and data extraction
