Web scraping is the process of collecting structured web data in an automated fashion. It’s also called web data extraction. Some of the main use cases of web scraping include price monitoring, price intelligence, news monitoring, lead generation, and market research among many others.
In general, web data extraction is used by people and businesses who want to make use of the vast amount of publicly available web data to make smarter decisions.
If you’ve ever copied and pasted information from a website, you’ve performed the same function as any web scraper, only on a microscopic, manual scale. Unlike the mundane, mind-numbing process of manually extracting data, web scraping uses intelligent automation to retrieve hundreds, millions, or even billions of data points from the internet’s seemingly endless frontier.
How do you use a data scraper?
Whether you’re using a data scraper tool yourself or outsourcing the job to a web data extraction specialist, you’ll need to know a bit more about the differences between web crawling and web scraping. Just as importantly, you’ll need to understand the possible pitfalls of extraction and how to avoid them. Read on to find out how web scraping works and how to achieve it successfully.
Web scraping is popular
And it should not be surprising because web scraping provides something really valuable that nothing else can: it gives you structured web data from any public website.
More than a modern convenience, the true power of data web scraping lies in its ability to build and power some of the world’s most revolutionary business applications. ‘Transformative’ doesn’t even begin to describe the way some companies use web scraped data to enhance their operations, informing executive decisions all the way down to individual customer service experiences.
What is data scraping good for?
Web data extraction – also widely known as data scraping – has a huge range of applications. A data scraping tool can help you automate the process of extracting information from other websites, quickly and accurately. It can also make sure the data you’ve extracted is neatly organized, making it easier to analyze and use for other projects.
In the world of e-commerce, web data scraping is widely used for competitor price monitoring. It’s the only practical way for brands to check the pricing of their competitors’ products and services, allowing them to fine-tune their own price strategies and stay ahead of the game. It’s also used as a tool for manufacturers to ensure retailers are compliant with pricing guidelines for their products. Market research organizations and analysts depend on web data extraction to gauge consumer sentiment by keeping track of online product reviews, news articles, and feedback.
There’s a vast array of applications for data extraction in the financial world. Data scraping tools are used to extract insight from news stories, using this information to guide investment strategies. Similarly, researchers and analysts depend on data extraction to assess the financial health of companies. Insurance and financial services companies can mine a rich seam of alternative data scraped from the web to design new products and policies for their customers.
Applications for web data extraction don’t end there. Data scraping tools are widely used in news and reputation monitoring, journalism, SEO monitoring, competitor analysis, data-driven marketing and lead generation, risk management, real estate, academic research, and much more.
The basics of web scraping
It’s extremely simple, in truth, and works by way of two parts: a web crawler and a web scraper. The web crawler is the horse, and the scraper is the chariot. The crawler leads the scraper, as if by hand, through the internet, where it extracts the data requested. Learn the difference between web crawling & web scraping and how they work.
The crawler
A web crawler, which we generally call a “spider,” is an artificial intelligence that browses the internet to index and search for content by following links and exploring, like a person with too much time on their hands. In many projects, you first “crawl” the web or one specific website to discover URLs which then you pass on to your scraper.
The scraper
A web scraper is a specialized tool designed to accurately and quickly extract data from a web page. Web scrapers vary widely in design and complexity, depending on the project. An important part of every scraper is the data locators (or selectors) that are used to find the data that you want to extract from the HTML file - usually, XPath, CSS selectors, regex, or a combination of them is applied.
What is a web scraping tool?
A web scraping tool is a software program that’s designed specifically to extract (or ‘scrape’) relevant information from websites. You’ll almost certainly be using some kind of scrape tool whenever you are collecting data from web pages programmatically.
A scraping tool typically makes HTTP requests to a target website and extracts the data from a page. Usually, it parses content that is publicly accessible and visible to users and rendered by the server as HTML. Sometimes it also makes requests to internal application programming interfaces (APIs) for some associated data – like product prices or contact details – that are stored in a database and delivered to a browser via HTTP requests.
There are various kinds of web scrape tools out there, with capabilities that can be customized to suit different extraction projects. For example, you might need a scraping tool that can recognize unique HTML site structures, or extract, reformat and store data from APIs.
Scraping tools can be large frameworks designed for all kinds of typical scraping tasks, but you can also use general-purpose programming libraries and combine them to create a scraper.
For example, you might use an HTTP requests library - such as the Python-Requests library - and combine it with the Python BeautifulSoup library to scrape data from your page. Or you may use a dedicated framework that combines an HTTP client with an HTML parsing library. One popular example is Scrapy, an open-source library created for advanced scraping needs.
The web data scraping process
If you do it yourself using website scraping tools
This is what a general DIY web scraping process looks like:
- Identify the target website
- Collect URLs of the pages where you want to extract data from
- Make a request to these URLs to get the HTML of the page
- Use locators to find the data in the HTML
- Save the data in a JSON or CSV file or some other structured format
Simple enough, right? It is! If you just have a small project. But unfortunately, there are quite a few challenges you need to tackle if you need data at scale. For example, maintaining the scraper if the website layout changes, managing proxies, executing javascript, or working around antibots. These are all deeply technical problems that can eat up a lot of resources. There are multiple open-source web data scraping tools that you can use but they all have their limitations. That’s part of the reason many businesses choose to outsource their web data projects.
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