Web scraping, aka Data harvesting, is gathering large amounts of information from the internet and storing it in databases for later analysis and use as per the requirement.
Web harvesting entails extracting data from search page results as well as a deeper search of the content concealed inside Web pages. Because of the HTML code, this additional information is frequently hidden from search engines. In order to extract valuable parts, the method scans material in the same way as human eyes do, eliminating characters that do not form coherent words.
When a web scraper wishes to extract a website, it first loads all of the HTML code provided for the website and extracts every available information on the website. Web scraping technique allows to extract non-tabular or poorly structured data from websites and convert it into a structured format, like .csv file or spreadsheet. Web scrapers can extract all of the information on a website or just the information a user wants. The scraper is given the instructions for the specific parts to be scraped in selective web scraping. Any site can be scraped; however, many seek to protect themselves from unwanted scraping. You can read the “robots.txt” file on most websites to see if they allow scraping.
The other term associated with web scraping is web crawling, both of the techniques are interrelated and primarily implemented together to serve the same purpose the web extraction. The role of crawlers is to crawl through all the web pages of the target site for indexing. In contrast, scrapers create a replica of the webpage and store it into the database by extracting every piece of information from the website.
Currently, most organizations are moving towards a data-driven approach for strategic decisions based on data analysis and interpretation.
Techniques like web Harvesting have excellent potential to play a vital role in the growth of every organization.
For instance, e-commerce sites scrape for the pages of their competitors to extract information about prices, details, etc. They then use this information to adjust their prices and implement strategies accordingly. Some of the significant usages of Web scraping includes-
- E-commerce price monitoring
- Machine learning model enhancement
- Sentiment analysis
- E-mail marketing
- Lead generation
If you know how to get it, this information can be highly beneficial for your organization. Scraping data, on the other hand, requires technical expertise, and it has some roadblocks which need to be rectified to scape the web successfully. Scraping is also done manually, which is quite a laborious process; the other way is to build a scraper that requires technical expertise and an appropriate proxy server.
In this article, we will go through the ways of scraping thousands of websites at once.
The web harvesting operation by specialized software fetches data from the Internet and places it into files for an end-user. It provides a role similar to that of a search engine, but it is more advanced.
There are two well-known and widely used methods for scraping data from the web: generic web scraping software and writing code. You can use ready-made software or create your script. A variety of software tools are available to scrape data from the internet.
Web scraping software is further divided into two categories. The first can be installed locally on your computer, and the second is cloud browsed web application, like Crawlbase (formerly ProxyCrawl), which you don’t need to install on your system and access the complete web harvesting and crawling tools. You don’t need to worry about blocks and CAPTCHAs as the web scraping tools handle them independently.
The following are the features of web harvesting software:
- Text can be scraped from any website.
- Extract HTML code
- Retrieve images or diagrams from web pages
- Export extracted data to a spreadsheet, .csv and JSON
- OCR (Optical character recognition) for fetching texts
- Schedule and automate data extraction
While considering a web harvesting tool, one must keep a few factors in mind like-
Header Support: To scrape most websites, correct headers are required. If you want to access a site that requires headers, be sure that the scraping tool you use allows you to modify them.
Automation: Data filtering and extraction are automated in many online scraping tools. This is crucial functionality for web scraping if you don’t have another text filtering tool.
Integrations: Some online scraping tools integrate with analytics or cloud services directly, while others are self-contained. Choose a tool that allows you to combine your scrape data with existing data centers.
Unstable scripts are a genuine possibility, as many websites are still under construction. Your scraper might not be able to explore the sitemap correctly or find the required information if the site’s structure changes. The good thing is that most website modifications are minor and incremental, so you should be able to update your scraper with minor changes.
Through a reliable web scraper tool, we can extract as much data as we want. Some scrapers offer asynchronous service where you will be feeding links to it, and it will give you the scraped data at the same time in your webhook or your prescribed format. Web scraping can be done on a single website and multiple websites as well. The scraper is fed with the URLs of the websites which need to be scrapped, and then the structure decides to store scraped data.
The usage of scraping varies as per the user’s requirement, such as scraping a single website, scraping various crawled links of a website, or scraping multiple websites at a time.
If you need to scrape just one website, you can put the URL of that website in the Scraper API and hit the scrape data button.
Here we have scraped TechCrunch using the generic scraper, and the Scraper API will fetch the scraped output in JSON format.
It’s simple to scrape data from a single webpage by simply giving the URL to the scraper API and then copying the scraped data you need and saving it to your PC; But what if you need to extract thousands of websites? Will the same methodology work?
You may need data from numerous pages on the same website or multiple separate URLs, and manually creating code for each page is time-consuming and laborious. The simplest way of scraping multiple pages is by creating loops of the URLs.
We will look at two basic approaches for extracting data from multiple web pages using Python:
- Multiple URLs from the same website
- Different website URLs
The approach of the program will be pretty simple for the multiple URLs from the same website:
- All of the necessary libraries will be brought in.
- Using the requests library, set up URL strings for creating a connection.
- The BeautifulSoup library’s parser is used to extract the accessible data from the target page.
- Identify and extract the classes and tags that hold valuable information for us from the target page.
- Use a loop for prototyping it for one page, then apply it to all the pages.
The pages on most websites are labeled from 1 to N. Because they all have identical architecture, looping through them and extracting data is straightforward.
The process presented above is excellent, but what if you need to scrape multiple sites and don’t know their page numbers? You’ll have to go through each URL one by one and manually develop a script for each one.
You could create a list of these URLs and loop through them instead. We can extract the titles of those pages by simply iterating the elements in the list, i.e., the URLs, without having to create code for each page.
Multiple websites can be scraped using Crawlbase (formerly ProxyCrawl) by creating the loop of URLs; the user should select the appropriate token for the Scraper API. Crawlbase (formerly ProxyCrawl) also offers various readymade scrapers for major e-commerce sites like Amazon, eBay, and Walmart; there are many others. With the help of these readymade scrapers, we can easily extract data from multiple pages of these sites. You can opt for the generic scrapers for different websites and scrape a massive number of web pages.
The scraper API loop extracts information out of multiple web pages by using “URL list Loop.” It embeds with almost every programming language. You can feed it the URL list in JSON/CSV format.