How Web Scraping Works?

How Is Web Scraping Done

You’ve got a business case for big data analysis and settled on web scraping as the means to get the level of accurate and detailed information you need. What’s next?

This guide delves into how exactly web scraping works, and what kind of data you can get.

What kind of data can be scraped from the web?

Why web scrape?

There are many reasons a business might want to extract information from websites, from monitoring their brand and sentiment analysis, to price tracking, to the automation of manual tasks. Basically, web scraping allows you to collect anything from websites that you would be able to copy and paste, but on a much larger scale.

The data that is scrapeable is almost boundless. Ten of the most popular information points that people use web scraping for are:

  1. Product information from e-commerce websites, for example, event ticket listings
  2. Business information from directories or social media platforms
  3. News articles, headlines, and other content from news websites and blogs
  4. Job postings and candidate resumes from job boards and recruitment websites
  5. Real estate listings from real estate websites
  6. Social media data from various social platforms
  7. Financial data from financial websites and news sources
  8. Contact data potential leads for marketing
  9. Travel data from travel booking websites
  10. Research data from academic databases and research websites

A real-life example of web scraping contributing to success was a project Datamam ran for a prominent corporation in the ticketing industry. The company approached Datamam with the objective of automating the process of scraping and matching data from its competitors. There were some challenges: an enormous volume of data, and sophisticated anti-bot measures employed by these platforms.

Datamam built a custom web scraping tool, carefully tailored to the client’s needs, to scrape the information necessary for them to make informed, rapid decisions and respond more effectively to market changes.

What does data look like before it’s scraped?

The data on a website is fundamentally stored and displayed through a combination of several key technologies:

  • HTML (Hypertext Markup Language) forms the backbone, structuring the textual and navigational elements of the page
  • CSS (Cascading Style Sheets) is essential for styling, determining how the site’s elements look in terms of layout, colors, and fonts
  • JavaScript plays a crucial role in adding interactivity and dynamic content, enabling websites to respond to user actions in real-time
  • Websites also often utilize multimedia elements such as images (.jpg, .png), videos, and animations to enhance visual appeal and user engagement

APIs (Application Programming Interfaces) are the bridges between websites servers and users’ browser, populating them with data of different structure and format powering information flow to the user. Also it is quite common to use API for micro tasks like user authentication.

Datamam, the global specialist data extraction company, works closely with customers to get exactly the data they need through developing and implementing bespoke web scraping solutions.

Sandro Shubladze, Founder & CEO of Datamam, says:

“The kind of big data collection that web scraping allows can have massive benefits for businesses, and its effective analysis can contribute to long-term success.”

“Successful web scraping can make this data collection considerably more efficient, and massively improves the quality and relevance of the data collected. Monitoring competitors, the broader market and your own brand not only keeps business leaders well-informed, but also allows for more accuracy and can even contribute to lead generation.”

How to scrape data from a website

Web scraping itself is completely legal, but there are some players out there who might deliberately abuse the process and use it for malicious purposes, or accidentally end up with personal data that violates privacy laws. It is important that you have all the information about the potential legal and ethical pitfalls before you start – you can find more detail about this in our ‘What is Web Scraping?’ article.

Web scraping plugins are one option, but they will have limited data sources which could cause the extracted data to be inaccurate and incomplete. These tools also lack flexibility for more advanced needs, and could also run the risk of security issues, and legal and ethical implications.

To avoid these potential challenges completely, consider using a web scraping specialist company such as Datamam, rather than self-building. Datamam can support companies with planning and developing a bespoke solution, supporting you through the process from beginning to end to get exactly what you need.

While there are many ways to go about building a web scraper, generally, the steps are:

1.    Planning

Properly planning for your web scraping project is crucial to getting the results you want. You will need to decide the parameters of the information you need, exactly the types of websites you would like to target, and the project scale. You will need to decide what type of web scraper you want to build, and which programming language and framework to base it on.

Web scraping involves using programming languages like Python, Java, or JavaScript. These languages rely on specific libraries or frameworks to ask for information and make the browser automatically navigate through web pages. Python, a general-purpose language, is popular to use because of its simplicity, easy integration with data analysis tools, extensive resources and cross-platform compatibility.

It is up to the project manager which programming language and framework is used, and the choice is usually based on things like the specific requirements, how well the individual or team knows the language, and the characteristics of the websites being scraped.

2.    Setting up

Once all your planning is complete, you’ll need the web scraper to be set up. This involves writing the scraper code, implementing error handling, and automating the tool. It is important to test the web scraper on a small scale first, to iron out any issues and ensure it is able to get the information you need.

3.    Sending requests and receiving responses

Once everything is set up, the next steps will be automated. The web scraping tool asks the website for information through an HTTP request to the targeted website’s server. This request asks for the HTML, visual or API content of a specific web page, which should include all the information necessary.

The web scraper will be able to automate these requests to ask for information from hundreds or even thousands of websites in the blink of an eye.

Quite quickly, the websites then start the process of the request and responding with the relevant content from the requested web page. This can be done in a matter of seconds.

4.    Extraction and retrieval

Then the web scraper really gets to work. It starts to locate and extract all the relevant information that it has been programmed to look at, from the HTML, visual data or API that it’s been sent.

Once the elements containing the desired data are identified, the scraper extracts the information from those elements. This could include text, images, links, or other types of content.

The information is parsed – or translated – into a readable format and downloaded. This is usually in CSV or JSON form, which allows it to be easily sent between computers. CSV or JSON files are made up of plain text, and can easily be opened in a spreadsheet or notepad.

5.    Utilization

The final stage is the really exciting part. The processed data can be stored in a database to be shared with other interested parties, or used immediately for analysis, reporting, or anything else.

Can anyone scrape the web?

That’s it! It sounds simple, but there are lots of things to take into consideration as you go. If a project isn’t properly planned it could end up extracting the wrong information, which may turn out to be costly and a waste of time.

Setting up a self-built web scraper will require existing Python knowledge, while browser extensions and other basic tools may not be able to scrape the data in the way you want. Datamam, the global specialist data extraction company, works closely with customers to get exactly the data they need, through a bespoke web scraping offer.

“Web scraping has so many potential benefits, but for those looking for really accurate data it’s not always as straightforward as it appears to DIY,” says Sandro Shubladze.

“Those businesses that are looking for the most efficient extraction and most accurate data can lean on a professional team to do it for them. Custom web scraping takes a strategic approach which allows for tailored data extraction.”

Creating a custom web scraper gives you the flexibility to handle changes in website layouts, making it a scalable solution for your evolving data needs. Sure, there might be some upfront development costs, but the long-term benefits like better data quality, adaptability, and a competitive edge make it a smart investment for organizations looking to get the most out of web insights.”

Scrape Website

Can any website be scraped?

Some websites are harder than others to extract data from, as some organizations put mechanisms in place to guard against scraping. The series of measures and techniques employed by websites to detect web scraping are known as ‘anti-scrape handling’.

What anti-scrape security do organizations use?

These organizations are looking to protect themselves from malicious or harmful forms of web scraping. Some forms of malicious scraping can violate a website’s terms of service and potentially lead to misuse of data, and badly optimized bots can slow down or even break websites – anti-scrape technology can protect against issues such as these.

Some of the multi-layered security strategies organizations put in place to prevent malicious bots include:

  1. IP Detection tools to scan IP addresses, identify and block malicious activities
  2. CAPTCHA puzzles to distinguish human users from bots
  3. User Profile Analysis examines and detects irregularities in account creation
  4. User Cookies track user interactions over time, identifying bots with no consistent cookie history
  5. User Digital Fingerprinting to analyze browser characteristics and device information, spotting inconsistencies
  6. Honeypots like invisible links can identify bots
  7. Robots.txt directives advise bots on where they are able and unable to crawl, differentiating the malicious from the well-intentioned
  8. Filtering requests, to ensure the legitimacy of website users

Organizations can use a mix of these strategies for their websites to form their anti-scrape security mechanism, through identifying bots and disrupting the scraping process.

Can anti-scrape security be bypassed?

There are techniques to navigate security structures such as these.

One is through utilizing ‘proxies’, the intermediaries between the scraper and a website. Proxies help keep the scraper’s identity hidden by changing its IP address, protecting privacy and avoiding detection. They rotate IP addresses to prevent being blocked for too many requests and allow multiple requests from different IPs at the same time for better scalability. Proxies also assist in accessing region-specific data and handling websites with session-based authentication.

Another way to bypass security set up against malicious web scraping is through emulating standard user interactions within the browser. This involves setting up the web scraper to automate ‘human’ actions like clicking buttons, filling forms, and scrolling. This is useful for scraping dynamic or JavaScript-heavy websites. It enhances accuracy by simulating user engagement.

Finally, web scrapers can gather essential cookies to avoid bot detection. Setting up and optimizing custom cookies as part of the web scraping process to resemble human interactions will make it less likely that web scraping bots will be blacklisted.

Each of these security bypass strategies must be undertaken responsibly, respecting website policies.

“Efficiently navigating security measures in web scraping requires a delicate balance between respecting the protections put in place by websites and obtaining the valuable data you need,” says Sandro. “It’s absolutely vital to understand and follow both the ethical and legal boundaries of the web scraping process.”

“There are responsible scraping practices organizations can put in place, to mitigate the risk of triggering security measures. These measures should be considered as something to approach with integrity and in a transparent manner, rather than as obstacles to simply overcome. Responsible web scraping means avoiding disruption to websites you are extracting data from, and prioritizing privacy.”

And that’s it. You’re now clued up on how to web scrape!

Getting the more accurate and high-quality data can be a complex process, but the business benefits of having data such as this are endless.

Datamam can be your trusted partner in getting your project off the ground. Get in touch via our contact page to find out more.

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<a href="" target="_self">Sandro Shubladze</a>

Sandro Shubladze

Building a World Inspired By Data

My professional focus is on leveraging data to enhance business operations and community services. I see data as more than numbers; it's a tool that, when used wisely, can lead to significant improvements in various sectors. My aim is to take complex data concepts and turn them into practical, understandable, and actionable insights. At Datamam, we're committed to demystifying data, showcasing its value in straightforward, non-technical terms. It's all about unlocking the potential of data to make decisions and drive progress.