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Arpan Jha

It’s been more than a decade since we touched upon the difference between data scraping and data crawling. It was one of the most visited pages ever on our website. And even after 12 years of talking about it, web scraping is more relevant than ever. Primarily because it has become integral to most business operations across industries. More and more businesses have realized that just one-off data collection to derive an insight for a marketing strategy or for a pricing decision isn’t going to cut it. Web scraping vs data extraction – let’s learn the real difference.

The Real Difference

To put things in perspective, let’s talk a little bit about what exactly web scraping entails and how it’s different from data extraction conceptually. Web crawling is usually a term for collecting data from the web using bots as opposed to manually. A bunch of programs, called bots or crawlers, automatically download web pages and save them in a uniform format for further processing/analysis. Since these are programs, they could be running 24×7, in the background, on autopilot once you’ve told it what pages to download, in what format, and where to save them. After these pages are collected, there are a different set of programs employed to parse these pages into meaningful information or what we call “structured data”. For instance, the bots could have downloaded a bunch of product pages from Amazon, and then parsed the pages to retrieve product name, product specifications, price, discounts, color options and more. This phase is what is known as Data Extraction. Data extraction requires precision and needs customization for every page that’s of interest to a user. Product pages have a different template than let’s say a review page, and if one is interested in analyzing thousands of reviews on an Amazon bestseller, along with its product page, they will need 2 separate parsers for each. Now this entire process of downloading, parsing and processing is collectively known as web scraping.

Web Scraping = Web Crawling + Data Extraction

Both crawling and data extraction are subsets of scraping. Web scraping vs data extraction, what’s the difference then? You’ll notice both web scraping and web crawling being used interchangeably, even though the former involves deriving structured data out of the pages crawled.

Why is Web Scraping so Difficult?

There are 3 main reasons why web scraping is a big deal and needs specific expertise:

  1. Scale– Enterprise Data as a Service players like PromptCloud cater to large and recurring needs of enterprises. We are talking about billions of pages to be downloaded from tens of thousands of sources (which don’t share the same design templates) and later extracting custom information as per the use case on a daily basis. This requires a full-fledged state-of-the-art tech stack inbuilt with enough distributed clusters, queuing systems, processing power, indexes and other knick knacks.
  2. Website Dynamics– Websites are ever-changing; a parser that worked yesterday might not work today, and yet the need to acquire the data doesn’t change. Besides, with the evolving webdev landscape, and the fancy elements built in, crawling the websites and extracting data from them is becoming increasingly complex.
  3. Precision– For a business use case, one can’t afford a single instance of a price column having a product name or vice-versa for example. And hence precision extraction is crucial to the whole data acquisition pipeline. It demands intelligent programming and multiple layers of quality checks-both automated and manual.

Why Does a Business Need Web Scraping?

There are numerous use cases of how all of this publicly gathered data from the web could be of use to a business ranging from eCommerce to travel to research. Some of the most interesting ones that we have helped our customers with are listed below.

  • Identifying MAP (minimum advertised price) violations on retailer websites for a brand
  • Navigating freight routes of the popular freight companies across the globe to provide optimization recommendations
  • Analyzing sentiments across social media for a brand and helping brand owners respond quickly
  • Helping airlines understand the most popular routes for their revenue optimization efforts

Final Note

While there’s no going back now on the need to be a data-driven business, being a good bot on the web is still important. As a bot, respect a domain’s robots.txt file- don’t access the pages it doesn’t want you to access and hit the domain only as often as is directed. Or just hit us up and leverage our decade-long expertise in this space. 

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