What is the difference between scraping and crawling?
In the age of Big Data, it’s crucial to learn to take advantage of all the information that surrounds us. But to do so, we first need to gather the required data and pick out the important parts. It’s a very complex process that requires quite a lot of time and resources. That’s why we’ve taught computers to do that for us.
There are two terms related to data gathering - web crawling and web scraping. And people often confuse them. These approaches have their similarities. Yet these are two completely different processes that chase different goals. So let’s see what each of these terms mean, and how you can use these methods to improve your business.
What is web crawling?
This process lies in the core of any search engine like Google. Web crawlers are bots that constantly monitor the Internet looking for new web pages. Then they go through them, analyze the content and links. Web crawlers are somewhat indiscriminate, which means they will check and gather all the data.
Once crawlers analyze the gathered data, they can determine rankings of the site and topics related to it. Thus, when you give a search engine certain words looking for something, it will fetch you the information you need.
What is web scraping?
This process is also executed by bots, except they extract only specific data. If you don’t have a list of URLs that might contain the information you need, you will have to perform web crawling first. As you acquire the URLs, you can switch to scraping.
Scrapers will go through all the content picking out the information you asked them to fetch. Usually, scraping aims at structured data such as price comparisons, emails, company names, URLs, or phone numbers. Then you can parse, search, and format this data to structurize it and make it easy to work with.
So even though scraping and crawling are often used as interchangeable terms, they differ. While crawling brings you general data, scraping fetches more precise information that can be processed properly.
How can I scrape the data?
Back in the day, you would need to gather all the data manually and copy-paste it into Excel or somewhere else. Fortunately for us, today, numerous programs will let you gather the information you need.
Efficient scraping software should combine crawling and analysis. First, it should gather the data by web-crawling just like search engine robots do. And then, the system should analyse the general data gathered by crawlers, sort it, and present it in the form that will be convenient for you to consume.
More advanced point-and-click programs are enhanced with Artificial Intelligence. DiffBot is an example of such a tool. AI-based scrapers will successfully fetch you data from common kinds of sites like blogs, e-commerce websites, and so on. But they won’t work well with complex sites as well.
If your project requires more advanced tools, you should hire a developer that will build a unique scraper for your needs. Although there is an issue as well. Devs have different approaches to building scrapers, so the result can be not as good as you’ve expected it to be. Another solution would be to use more complex and advanced services like Import.io or Mozenda. They’re quite expensive, and smaller businesses simply can’t afford them.
Why do I need proxies for scraping?
Scrapers are bots, and most websites and especially search engines, do everything they can to protect the content from scraping. Bots send numerous requests quickly, and all inquiries are sent from a single IP address. That’s how servers understand that they’re dealing with bots. Consequently, they ban that IP address from which all the traffic comes.
Obviously, the solution is to hide your real IP address. That’s what proxies are for. They will mask your authentic IP with another one. Moreover, you can set up the scraper so that it will use another proxy with each request. Then the traffic will seem somewhat normal, and the target server won’t be alarmed.
Web scraping is still on its early stage, so we can expect such tools to evolve and become more efficient. But before that, we need to master the instruments we have and take advantage of the world of Big Data.
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