Data Scraping vs. Data Crawling: The Differences

To get down to the bottom of the web crawling vs web scraping battle, we must first define both concepts.

Let’s define the concepts

a) Data scraping

According to the definition, data scraping is a process of taking required publicly available data and importing the founded information into any storage on your computer. It is worth mentioning that data scraping does not require the internet to be conducted. There are several reasons businesses would like to scrape data; for example, you can scrape email leads generation, price comparison, SERP scraping, etc. It depends on the company you run. Keep in mind that the easiest way to do so is to use proxies. If you are looking for more information about the proxy and how you can use it for your business, you can find more information here.

If you can notice, the main difference between data scraping and web scraping is that web scraping needs an internet connection to work. There are three main steps to the web scraping process:

  • Request/response
  • Data parsing and extraction
  • Data downloading

b) Data crawling

According to the definition, data crawling is a process of data extraction. In other words, data extraction means collecting data from either the world wide web or data crawling cases – any document, file, etc. Usually, it is done on a large scale, but data crawling is not limited to small tasks.

Web crawlers or data crawlers essentially search for two crucial things:

  • Multiple targets to crawl
  • Target information the user is looking for

The web crawling process includes several steps:

  • Choosing starting seed URLs
  • Adding URLs to the frontier
  • Choosing the target URL from the frontier
  • Fetching the corresponding web page
  • Parsing for new URLs
  • Adding new URLs to the frontier
  • Repeating step 3 until the frontier is empty

Now that we know both data scraping and crawling concepts, we can move on to the main differences between the two. If you are unsure or understand the differences between these concepts, we suggest you check out Oxylabs article on web crawling vs web scraping.

The main differences between

Let’s take a look at the main differences between data scraping and crawling:

  • Data scraping uses a data scraper while data crawling uses a data crawler, also known as a spider bot.
  • Data scraping aims to download information, whereas data crawling refers to the indexing of web pages.
  • Data scraping doesn’t involve visiting all target web pages to download data, while web crawling requires visiting each web page until the URL frontier is empty.
  • Data scrapers aren’t required to abide by the robots.txt rules, while data crawlers have to obey robot.txt always.
  • Data scraping is done on small and large scales, while data crawling is usually done on a large scale.
  • Data scraping is mainly used in machine learning, equity research, and retail marketing. On the other hand, data crawlers are used in search engines to provide the wanted search results.
  • Data scraping doesn’t necessarily involve de-duplication; however, it is an essential part of data crawling.
  • Data scraping requires a parser and scrape agent, and data crawling needs only one spider bot.

The most often use cases

When it comes to data scraping for business, there’s no denying that it’s present in pretty much every business area. Being able to acquire accurate and relevant data successfully is an integral component of getting ahead of the competition.

Some of the business areas include:

  • Pricing and competitor analysis – businesses are increasingly relying on data scrapers to come up with a pricing strategy. Scrapers can help find, collect, and extract the pricing data of competitors and track their online behavior, discounts, and pricing tactics.
  • Sales and marketing – scraping can be beneficial for monitoring the web, extracting consumer reviews, and ratings from different online platforms, analyzing consumer behavior and interest, gathering high-quality leads, and tracking competition.
  • Product development – e-commerce websites are an abundant source of most excellent data regarding product descriptions. Scrapers can help collect this data, as well as check your stock status across multiple retail and market websites.
  • Risk, brand, and PR management – scraping can help a business monitor brand mentions, check advertisers’ landing pages, improve ad performance, and detect ad fraud to take the necessary steps.
  • Strategy development – data is the new currency in the modern business industry, and enterprises rely on data to develop effective business strategies. Scraping allows a business to stay on top of all the most popular trends and events in the industry, and improve SEO efforts.

On the other hand, data crawling helps a business:

  • Get better search rankings on Google and all other search engines.
  • Spiders crawl countless web pages to help generate results consumers are looking for.
  • Crawling bots assess and improve the quality of content and sort the web pages to improve user experience.
  • Crawlers provide consumers with the most accurate and relevant results to their queries.

Spiders are essential and integral to every SEO strategy, allowing businesses to drive more traffic, increase revenue, improve sales, lead generation, conversion, and customer retention rates.

How to use them in tandem

Using a combination of scraping and crawling for data gives a business the following information:

  • Product name
  • Product URL
  • Product description
  • Product category
  • Pricing data
  • Brand information
  • Stock levels
  • Manufacturer part number
  • Product image

For the sake of understanding the potential we’re talking about here, this is just the tip of the iceberg. Scraping and crawling are essential for any online business today.


Since both scraping and crawling are quite related processes, it’s no wonder that people get confused about it.

However, we sincerely hope that we managed to shed some light on the matter and point out why it’s essential to consider investing in both of these data acquisition techniques. Each has a huge potential to offer, and using both is a sure way to get ahead of your competition.