In the modern age, the need for data continues to increase. With the volume of data ever increasing at a logarithmic rate, data analytics has become essential for organizations to survive.
Although, there are different sources via which data can be extracted. But with technology growing and new tools coming into the market, data extraction has become easy. One such technique of extracting data from the web is known as web scraping.
Web scraping is also known as data scraping, data extraction, or web harvesting. The main goal of it is to collect data from websites, convert it into a desired format, and save it for future use.
In this article, we might be using web scraping or data scraping in different places, but they have the same context altogether.
A good example of web scraping and crawling is search engines, which continuously crawl and store the data on the internet to build a database of websites and their content. This enables search engines like Google to quickly and easily provide users with relevant results. Without search engines, navigating the web would be much more difficult.
Price scraping is another widely known use case of web scraping. This involves regularly extracting data from a list of e-commerce websites, including both existing and new products by dedicated data scrapers.
This extracted data is then aggregated and transformed into a standardized data format that can be used for specific use cases. One popular application of this data is providing a price comparison service across all these merchants.
By leveraging the extracted data, users can easily compare prices across different e-commerce platforms and make informed purchasing decisions.
While search engines and price comparison are common examples of data scraping, there are many other applications as well.
History of Web Scraping
Web scraping has its roots in the creation of the World Wide Web by British scientist Tim Berners-Lee in 1989. Originally, the purpose of the web was to facilitate the sharing of information between scientists at universities and institutes worldwide. However, the World Wide Web also introduced several key features that are crucial to modern data scraping tools.
These include URLs, which enable scrapers to target specific websites, embedded hyperlinks that allow for easy navigation, and web pages containing various types of data such as text, images, audio, and video.
Following the creation of the World Wide Web, Tim Berners-Lee developed the first web browser in 1991. This browser was an http:// web page that was hosted on a server running on his NeXT computer. With this browser, people gained the ability to access and interact with the World Wide Web.
In 1993, the concept of web crawling was introduced with the development of the World Wide Web Wanderer by Matthew Gray at the Massachusetts Institute of Technology. This Perl-based web crawler was designed to measure the size of the web.
In the same year, the Wanderer was used to create an index called the Wandex, which had the potential to become the first general-purpose search engine for the World Wide Web. Although the author did not make this claim, the technology could perform this function.
The very same year JumpStation was also developed, and it became the first web search engine based on crawling technology. This groundbreaking technology laid the foundation for modern search engines such as Google, Bing, and Yahoo. With JumpStation, millions of web pages were indexed, transforming the internet into an open-source platform for data in various forms.
In 2004, a Python programming library called BeautifulSoup was introduced, which allowed for easier parsing of HTML structure and content.
As the internet grew into an immense source of information that was easily searchable, people started taking advantage of the available data by extracting it. Initially, websites did not prohibit the downloading of their content, but as more data was being downloaded, manual copy-pasting was no longer a feasible option. This prompted the development of other methods for obtaining information.
So How Data Scraping is Done?
Data scraping involves making HTTP requests to a website’s server to retrieve the HTML or XML source code of a webpage and then parsing that code to extract the data you are interested in.
Web scraping can be done manually, by writing code to make HTTP requests and parse the HTML or XML source code of a webpage, or it can be done using a web scraping tool or software. Some web scraping tools are designed to be easy to use, with a simple point-and-click interface, while others are more advanced and require programming skills to use.
Extracting data manually can take lots and lots of hours, workers, costs, and much more inputs. Web scraping can be useful for automating tasks that would be time-consuming or difficult to do manually.
For example, if you need to gather data from multiple websites regularly, you could write a web scraper to do the job for you. This would save you the time and effort of manually visiting each site and copying the data you need.
Applications of Web/Data Scraping
Web scraping is used for a variety of purposes, including:
Data mining:
Data scraping tools can be used to extract large amounts of data from websites and then analyze it to uncover patterns, trends, and insights. This can be useful for research, business intelligence, and other data-driven purposes.
Read More: How web scraping can help in market research for your product
Price comparison:
Web scraping can be used to gather data from multiple online retailers and compare prices on products. This can help consumers save money by finding the best deals, and it can also be useful for businesses looking to track prices and trends in the marketplace.
Lead generation:
Data scrapers can be used to gather contact information for potential customers or clients from websites and other online sources. This can be useful for sales and marketing efforts.
Read More: Benefits and Advantages of Lead Generation for your Business via Web Scraping
Content aggregation:
Web scraping can be used to gather data from multiple sources and combine it into a single, cohesive whole. This can be useful for creating news aggregators, social media feeds, and other types of content-rich websites.
Online reputation management:
Web scrapers can be used to gather data from review sites, social media, and other online sources to track a company’s reputation and identify areas for improvement.
Overall, web scraping can be used for a wide range of purposes, and the specific applications will depend on the needs and goals of the user.
Web scraping can also be useful for tasks that involve processing large amounts of data. For instance, if you need to analyze data from a large number of web pages, it would be much more efficient to use a web scraper to extract the data and process it automatically.
Overall, web scraping can be a useful tool for overcoming manual efforts and streamlining tasks that involve collecting and processing data from the web. It can save time, reduce errors, and allow you to focus on other tasks while the scraper handles the data-gathering work.
Best Practices for Doing Web Scraping
Continuously parse & verify extracted data
After extracting data from a website, it is important to parse it into a more readable format such as JSON or CSV for further analysis by data scientists and developers. Data parsing involves converting the collected data from its original format to a more structured one. This step is necessary because data from different websites often come in different formats that are difficult to understand.
To ensure that the parsing process is working correctly, it is recommended to verify the parsed data regularly.
This can be done automatically or manually at regular intervals. Failing to do so can result in collecting thousands of pages of useless data due to websites identifying bot traffic and serving misleading data to the crawler. Therefore, it is crucial to identify any issues early on in the process to avoid wasting time and resources.
Choose the right tool for Data scraping
You can build your custom scraper or can use a pre-existing web scraping tool for your needs.
Building a scraper of your own
Python is a popular programming language for creating web scraping bots, especially for beginners. Its large and active community makes problem-solving easier. With a wide range of web scraping libraries, such as Beautifulsoup, Selenium, and Scrapy, among others, you can choose the most appropriate one for your project.
The following five steps can guide you in creating your web scraper using Python:
- Identify the website from which you want to extract data.
- Inspect the webpage source code to locate the elements containing the data you need.
- Write your web scraping code.
- Execute your code to request a connection to the targeted website.
- Save the extracted data in the desired format for further analysis.
- Depending on your specific requirements, you can customize your web scraper accordingly. However, building a web scraper can be a time-consuming process, as it requires manual effort.
Read More: Create a web scraper of your own using Python
Using a pre-built web scraper
There are many pre-built data scrapers available that are open-source or require low/no code. With these tools, you can easily extract data from multiple websites without needing to write any code. These web scrapers can also be integrated as browser extensions, making them more convenient to use.
If you have limited coding skills, these low/no-code web scrapers can be particularly useful for your tasks.
Respect Robots.txt
Robot.txt is a file that provides guidelines for web scrapers on how to crawl pages on their site. These guidelines may include rules on acceptable behavior, such as which pages can and cannot be scraped, which user agents are allowed or not allowed, and how frequently and quickly you can do it.
Before attempting web scraping, it is advisable to review the website’s robot.txt file, which is typically found in the root directory. It’s also a good idea to read the website’s terms of service to ensure compliance with their policies.
Send Request Through Proxies
When you send a request to a website’s server, they will be able to log and track your activity on their site. Websites also have a limit on the number of requests they can receive from a single IP address within a certain timeframe, and exceeding this limit can result in your IP address being blocked.
To avoid being blocked, it’s recommended to use a proxy network and regularly rotate the IP addresses being used. While free IPs are available for experimental hobby projects, for serious business use cases, a reliable and smart proxy network is necessary. There are several methods for changing your outgoing IP address.
a). VPN
A VPN can assign a new IP address to mask your original one, providing anonymity and enabling access to location-based content. While VPNs are not designed for large-scale business web scraping, they can be useful for individuals who need to remain anonymous. For small-scale use cases, a VPN may be sufficient.
b). TOR
TOR, or the Onion router, directs your internet traffic through a global volunteer network with thousands of relays, effectively hiding your location. However, using TOR for web scraping can significantly slow down the process, and it may not be ethical to place additional load on the TOR network. Therefore, for large-scale web scraping, TOR is not recommended.
c). Proxy Services
Proxy services are designed to mask your IP address, especially for business purposes. They usually have a vast pool of IP addresses to route your requests, making them more reliable and scalable.
There are different types of proxies available based on your use case and budget. Shared proxies, residential proxies, and data center proxies are some of the commonly used ones. While residential proxies are highly efficient for sending anonymous requests, they are also the most expensive and are typically used as a last resort.
Don’t follow the same crawling pattern
Web scraping bots and human users have distinct characteristics. Humans are slower and less predictable than bots, whereas bots are faster but more predictable. Anti-scraping technologies take advantage of these differences to block web scraping activities. Therefore, it’s recommended to incorporate random actions into your scraping bot to confuse the anti-scraping technology.
There is a lot more you can do. I have a separate blog on best practices of web scraping. Do check it out!!
Is web scraping legal? What are the risks involved?
In general, data scraping is not illegal. However, the legalities of web scraping may vary depending on the specific circumstances and the laws of the country in which it is being carried out.
One factor that can affect the legality of web scraping is whether the website owner has granted permission for the scraping to take place. Some websites explicitly prohibit web scraping in their terms of service, and it is generally considered a violation of these terms to scrape the site without permission. In these cases, the website owner may choose to pursue legal action against the scraper.
Another factor that can impact the legality of web scraping is the purpose for which the data is being used. In some cases, web scraping may be considered illegal if it is being used for malicious purposes, such as spamming, phishing, or stealing sensitive data.
Risks Involved
There are also several risks involved in web scraping, including the potential of getting banned or blocked by websites, the possibility of encountering errors or bugs in the scraping process, and the risk of being sued for violating a website’s terms of service or copyright laws. It is important to be aware of these risks and to take steps to mitigate them when engaging in web scraping.
Overall, the legality of web scraping depends on the specific circumstances and laws of the jurisdiction in which it is being carried out. It is important to be aware of the legal and ethical considerations surrounding web scraping and to obtain permission from website owners before scraping their sites.
Is web scraping a useful skill to learn in 2023 & Beyond?
Yes, web scraping is a useful skill to learn in 2023. Web scraping allows you to extract data from websites and use it for a variety of purposes, such as data mining, data analysis, and machine learning. With web scraping, you can collect and structure data from websites, and use it to inform your business decisions or to create new products and services.
How One Can Learn Web Scraping?
There are many ways to learn web scraping. You can start by searching online for tutorials and resources, or by enrolling in online courses or workshops.
There are several ways to learn web scraping, depending on your background and the level of expertise you want to achieve. Here are a few options to consider:
Online tutorials and courses: There are a variety of online resources available that can teach you the basics of web scraping, including tutorials, videos, and courses. Websites like Udemy, Coursera, and edX offer a wide range of web scraping-related courses, and many are available for free.
Some popular choices for learning web scraping are Python, as it has many libraries to facilitate the process, therefore, a good starting point can be learning Python first, and then diving into web scraping.
Books: Another way to learn web scraping is through books. There are several books available that cover the basics of web scraping as well as more advanced topics. Some popular choices include “Web Scraping with Python: A Practical Guide” and “Web Scraping with Python and Beautiful Soup”
Practice: The best way to learn web scraping is by doing it yourself. Start with small projects and gradually build up to more complex projects as you gain experience and confidence.
Join online communities: Online communities, such as forums, Reddit, or Stack Overflow, can be a great resource for learning web scraping. These communities are a great place to ask questions, share knowledge, and connect with other people who are also interested in web scraping.
Hire a mentor: Another way to learn web scraping is by working with an experienced mentor. This can be done through an online mentorship program or by reaching out to someone in your professional network who has experience with web scraping.
Ultimately, the key to learning web scraping is to be persistent, patient, and to be willing to experiment and try new things. It’s important to be aware that web scraping can have legal implications, so familiarize yourself with the regulations and laws of the country you’re working with.
Languages One Can Learn to Do Web Scraping
Many programming languages can be used for web scraping, including Python, Ruby, and Java. It is also possible to use specialized tools, such as web crawlers, to extract data from websites.
Python – It is a popular choice for web scraping because of its simplicity, flexibility, and a large number of libraries and frameworks available for web scraping. It makes it easy to send requests, parse HTML, and XML, and navigate the structure of a webpage.
Javascript – JavaScript can also be used for web scraping, particularly for scraping single-page applications that use JavaScript to dynamically load content.
Java – It is another popular choice for data scraping particularly in large-scale projects.
R – R is widely used in data analysis, data visualization, and machine learning. It is also suitable for scraping websites.
Other languages like PHP, Ruby, Perl, etc., can also be used for web scraping, depending on the specific requirements of the project.
Know More: Best Programming Languages for Web Scraping
Approximate Time of Learning Data Scraping
In terms of the time it takes to learn web scraping, it depends on your background and the amount of time you are willing to devote to learning. If you have some programming experience, you may be able to learn the basics of web scraping in a few days or weeks.
If you are a complete beginner, it may take longer to learn the necessary skills. In general, it is a good idea to set aside dedicated time each day or week to practice and learn web scraping. We have written some tutorials on web scraping with different programming languages. You can check them out:-
- Web Scraping with Python
- Web Scraping with Nodejs
- Web Scraping with C#
- Excel Web Scraping
- Web Scraping with Java
- Web Scraping with R
- Web Scraping with PHP
Conclusion
In conclusion, data scraping is a powerful technique to automatically extract information from websites.
It is important to use web scraping responsibly and be mindful of the website’s performance to avoid any legality issues. Overall, it can help you to improve your products and services, analyze customer feedback and stay ahead of the competition.
