Web Scraping vs Web Crawling: What’s the Difference?
Web Scraping vs Web Crawling: What’s the Difference?
In this article, read an explanation of the differences between web scraping and web crawling.
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Confused about web scraping and web crawling? Well, don’t worry. You're not alone.
Many people find it difficult to identify the difference between web scraping and crawling.
Why the confusion?
It’s because of web scraping and web crawling, if not absolutely identical, are similar and even the same to some extent. Both have similar use cases as well.
While the web is full of references to web scraping and crawling, it would not help until you read its definition in a simpler language.
Here are the definitions of both:
You may also want to read: 8 Awesome PHP Web Scraping Libraries and Tools
What Is Web Scraping?
Web scraping is basically extracting data from websites in an automated manner.
It is automated because it uses bots to scrape the information or content from websites.
It’s a programmatic analysis of a web page to download information from it.
Data scraping involves locating data and then extracting it. It does not copy and paste but directly fetches the data in a precise and accurate manner. It does not limit itself to the web; data can be scraped virtually from anywhere it is stored. It does not have to be from the Internet. It is about data and not where it is stored.
- Example of Web Scraping
Web scraping would involve scraping specific information from a particular web page or pages.
For example, you want to work on price intelligence. You would extract the price of various/specific products from Amazon or any other e-commerce site.
This would qualify as web scraping. Likewise, you can extract data and use it for business leads, stock market data, real estate listings.
What Is Web Crawling?
The term crawling comes from the way a spider would crawl. That’s why a web crawler is also sometimes called a spider. It’s basically an internet bot that systematically browses (read crawls) the World Wide Web, usually for the purpose of web indexing.
It is used for indexing the information on the page using bots also known as crawlers.
It involves looking at a page in its entirety and indexing it, including its last letter and dot on the page, in the quest for information.
Crawling through every nook and crevice of the World Wide Web, the spider locates and retrieves the information lying in the deeper layers. Web crawlers or bots navigate through heaps of data and information and procure whatever is relevant for your project.
Example of Web crawling
What Google, Yahoo or Binge does is a straightforward example of web scraping.
These search engines crawl web pages and use the information for indexing the web pages.
How Does Web Scraping Work?
The web scraping process follows the below 3 steps.
- The first step is to request the target website for the contents of a specific URL.
- In return, the scraper gets the requested information in HTML format.
2. Parse and Extract
- When it comes to Parsing, it usually applies to any computer language. It is the process of taking the code as text and producing a structure in memory that the computer can understand and work with.
- To put it simply, HTML parsing is basically taking in HTML code and extracting relevant information like the title of the page, paragraphs in the page, headings in the page, links, bold text, etc.
3. Download Data
- The final part is where you download and save the data in a CSV, JSON or in a database so that it can be retrieved and used manually or employed in any other program.
How Does Web Crawling Work?
Web Crawling Process follows below steps :
- Select a starting seed URL or URLs
- Add it to the frontier
- Now pick the URL from the frontier
- Fetch the web-page corresponding to that URL
- Parse that web-page to find new URL links
- Add all the newly found URLs into the frontier
- Go to step 3 and reiterate till the frontier is empty
Web Scraping Tools
There are countless web scraping tools in the market. But for this particular discussion, I will discuss only two of them.
ProWebScraper helps you extract data from any website. It’s designed to make web scraping a completely effortless exercise.
Its point-and-click interface is extremely user-friendly and makes your life easy as far as web scraping is concerned. You don’t need any technical knowledge to carry out complex web scraping tasks.
Webscraper.io is a chrome extension to easily get data from websites.
Using this extension, you can create a plan (sitemap) how a web site should be traversed and what should be extracted. Using these sitemaps the Web Scraper will navigate the site accordingly and extract all data. Scraped data later can be exported as CSV.
Web Crawling Tools
Out of several web crawling tools available in the market, I will discuss only the following two:
Scrapy is a high-quality web crawling and scraping framework which is widely used for crawling websites. It can be used for a variety of purposes such as data mining, data monitoring, and automated testing. If you are familiar with Python, you would find Scrapy quite easy to get on with. It runs on Linux, Mac OS, and Windows.
Apache Nutch is an enormously useful web crawler software project that you can use for scaling it up. It is particularly popular for its application in data mining. Data analysts, data scientists, application developers, and web text mining engineers extensively use it for their diverse applications. It is a cross-platform solution written in JAVA.
Application of Web Scraping:
In retail, there are numerous avenues wherein web scraping is being used. Whether it is competitor price monitoring or MAP compliance monitoring, web scraping is being utilized to extract valuable data and glean the vital insights from it.
Likewise, when it comes to an e-commerce business, one would need countless images and product descriptions that you cannot simply create overnight or copy and paste easily. Hence, web scraping comes in quite handy in extracting the wide variety of images and product descriptions for an e-commerce business. For online marketplace, you badly need web scraping to match the pace with the lightning-quick changes occurring every moment. In this way, web scraping has a large number of applications in retail marketing.
Equity research used to be limited to reading financial statements of a company and accordingly investing in stocks. But not anymore! Now, every news item, data point, and measures of sentiment are important in identifying the right stock and its current trend. How do you get hold of this kind of alternative data? That’s where web scraping helps. It can help you fetch all the data aggregation related to the market and enable you to look at the big picture. You can, of course, extract financial statements and all the conventional data from the websites in a much easier and faster way through web scraping.
Basically, machine learning is about enabling the machine to discover patterns and insights for you. However, for that to happen, you need to feed the machine a lot of data. Where’s the data going to come from? Yes, you are right; you will get it only from the web. Hence, web scraping is integral to machine learning because it can easily and quickly facilitate all kinds of web data in a reliable manner.
Application of Web Crawling:
Without web crawling, you wouldn’t have Google giving you search results in an increasingly more accurate and effective manner. Google crawls around 25 billion or more pages every day to give you the search results.
Web crawlers crawl the billions of web pages in order to generate results that users are looking for. As per changing user demand, web crawlers have to adapt to it as well.
Web crawlers sort the pages and also assess the quality of content and perform many other functions to carry out the indexing as an end result.
So as you can see, web crawlers are vital in generating accurate results.
Hence, web crawlers are integral to the functioning of search engines, our access to the World Wide Web and also serves as the first and foremost part of web scraping.
Web crawling and web scraping are related processes, hence it is possible to get confused about it.
But after reading this guide, I hope that you are perfectly clear about the definition, points of difference and use cases of both.
Once you are clear about the concept, you will be able to harness each one for your different needs.
Wishing you happy data crawling and data scraping!
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