In our increasingly data-driven world, big data is worth a lot of money. According to a new report from Research and Markets it’s projected that the big data market will grow from $162.6 billion in 2021 to $273.4 billion in 2026.
In order to gather data from publicly available sources such as websites, you’ll need to perform web scraping. While various web scraping tools are available, you could learn a useful programming language like Python and write unique code that will allow you to scrape websites quickly and accurately.
But what exactly is web scraping, and what are its various uses? In this article, we’ll answer these questions and provide you with actionable steps that will get you web scraping in no time!
What is Web Scraping?
Also known as web data extraction and web harvesting, web scraping is the process of extracting data from a website. While you can do this manually, when projects require extracted data from hundreds or even thousands of web pages, automated web scraping tools can do the job more quickly and efficiently.
Web scraping tools collect and export extracted data for in-depth analysis, typically into a central local database, spreadsheet, or API.
Web scraping software may access the internet either through HTTP or a web browser, with the web crawler and web scraper working together to extract specific data from the web pages. We’ll discuss web crawlers and web scrapers in greater detail later in this article.
Before data extraction can take place, it must fetch the webpage. Fetching refers to the process of downloading a web page. The browser does this every time a user visits a web page. The web page’s content is then parsed (i.e., analyzed for syntax), reformatted, or searched, with the extracted data then loaded into a database or copied into a spreadsheet.
What Is Web Scraping Used For?
Data scraping has numerous applications across many industries—including insurance, banking, finance, trading, eCommerce, sports, and digital marketing. Data is also used to inform decision-making, generate leads and sales, manage risks, guide strategies, and create new products and services.
Price Intelligence
Price intelligence refers to monitoring a competitor’s prices and responding to their changes in pricing. Retailers use price intelligence to maintain a competitive edge over their rivals.
Effective price intelligence involves web scraping, with eCommerce sellers extracting product and pricing information from other eCommerce websites to guide their pricing and marketing decisions.
Price intelligence remains one of the most prominent use cases for web scraping due to valuable data for revenue optimization, product trend monitoring, dynamic pricing, competitor monitoring, and other applications.
Market Research
Web data extraction plays a vital role in market research. Market researchers use the resulting data to inform their market trend analysis, research and development, competitor analysis, price analysis, and other areas of study.
Lead Generation
Businesses that want to attract new customers and generate more sales need to launch effective sales and marketing campaigns. Web scraping can help companies gather the correct contact information from their target market—including names, job titles, email addresses, and cellphone numbers. Then, they can reach out to these contacts and generate more leads and sales for their business.
Brand Monitoring
Brands increasingly use social listening and monitoring tools to gauge the public’s perception of their brands. You can use web scraping software to extract real-time data from various sources (including social media platforms and review sites). You can then analyze the aggregated data to gauge brand sentiment.
Business Automation
In some cases, you may need to extract large amounts of data from a group of websites. You need to do this consistently, quickly, and structured. You can use web scraping tools to automatically extract these data sets.
Real Estate
You need web data extraction to generate the most up-to-date and accurate real estate listings. Web scraping is commonly used to retrieve the most updated data about properties, sale prices, monthly rental income, amenities, property agents, and other data points.
Web scraped data also informs property value appraisals, rental yield estimates, and real estate market trends analysis.
Alternative Data for Finance
Web-scraped data is increasingly harnessed by investors to inform their trades and strategies. Use cases include: extracting insights from SEC filings, monitoring news and stock market performance, public sentiment integrations, and extracting stock market data from Yahoo Finance.
News and Content Marketing
Businesses, political campaigns, and nonprofits that need to keep a close eye on brand sentiment, polls, and other trends often invest in web scraping tools. Content and digital marketing agencies also use web scraping tools to monitor, aggregate, and parse the most critical stories from different industries.
How Does Web Scraping Work?
The web scraping tool makes HTTP requests to the website and then extracts data from the web pages. It parses publicly available content, which the server renders as HTML.
It also requests internal APIs for related data, such as product prices and SKUs. A database stores this data and delivers it to the browser through HTTP requests.
Successful web data extraction requires the work of two components: the web crawler and the web scraper. The crawler guides the scraper throughout the web pages and extracts the requested data.
The Crawler
The crawler (also known as a spider) is a form of artificial intelligence that trawls the internet to index and search for content. It does this by discovering and exploring links. A famous example of a spider is Google’s Googlebot.
This algorithm crawls websites and web pages to index the internet. The resulting index plays a considerable role in the search results you see whenever you use Google Search.
In web scraping, the first stage is typically crawling the internet or one specific website to discover URLs which you can then pass on to your scraper for a deeper analysis.
The Scraper
A scraper is a programmatic tool designed to extract data from a website accurately and quickly. They vary greatly in design and complexity. Scrapers contain data locators, which you can use to locate data that you want to extract from the HTML file.
How to Scrape Data From a Website
Here is a step-by-step, DIY guide to scraping data from a website:
1. Identify the Target URLs
Once you’ve identified the website you want to scrape data from, make a list of the target URLs (i.e., the web pages) you’ll be extracting data from.
Don’t forget to inspect the web pages to identify the specific data you want to scrape.
2. Make an HTTP Request to Access the Website
HTTP is an application layer protocol that you can use to structure requests and responses over the internet. HTTP uses a server-client model and requires data transfer via the network from one point to another.
The client can be your computer or smartphone, while the server is the web host ready to deliver the data after a successful request.
The server needs a GET response when the client asks the server for data. Note that different apps and programming languages have their own process for making HTTP requests.
In response to the HTTP request, the server sends the data and allows you to read the HTML or XML page.
3. Fetch Page Content From Target Urls (Data Fetching)
As previously mentioned, data fetching allows you to download the web page and display its contents on your screen.
4. Extract the Data From the Page (Data Parsing)
Once you’ve scraped data from the target URLs, you’ll need to parse it to make it more readable and conducive to data analysis.
Data parsing is necessary because raw HTML data isn’t easy to decipher. Data first needs to be rendered in a format that the data analyst can easily understand. This could mean creating data tables that show relevant information or generating reports from HTML strings.
5. Format the Extracted Data
You can then export the parsed data into a CSV, Google Sheet, or Excel spreadsheet. Many automated web scraping tools support formats like JSON, which you can use for APIs.
Web Scraping Best Practices
1. Respect the Robots.txt
A website’s robot.txt is a text file that instructs web scrapers on how to crawl the web pages on a website. Set up by the website’s administrators, rules could include specifying which web pages can be scraped, which user agents are not allowed, and how fast and frequently content can be crawled on the website.
It’s considered a good courtesy to check a website’s robot.txt before crawling it. This file is in the root directory of your website’s backend.
2. Use a Proxy
For unrestricted web scraping, it’s best to route your requests through proxies. This is necessary because when the target website’s server receives your request, it will log how many requests and actions the website performed.
All websites have a threshold for the number of requests they’ll accept from a single IP address. Once it reaches the threshold, the website will block the IP.
Proxies—VPNs, TORs, and other proxy services—allow you to get around this problem. The proxy network will rotate IPs frequently and enable you to scrape unlimited URLs on the target website.
3. Use a User Agent
User agents are computer programs that represent a person. Examples of user agents include web browsers, web scrapers, and download managers.
If you don’t use a user agent in your request, the target website will assume you’re not real and prevent you from accessing the content.
4. Rotate User Agents
Swapping common user agents between subsequent requests will help you scrape content without getting blocked. User agent lists are publicly available online.
5. Use a Slower Crawl Rate
Human scrapers are slow, unpredictable, and make occasional errors. Web scraping bots, in contrast, are fast and efficient. Anti-scraping technologies will be able to detect bot behavior and will prevent you from scraping. To prevent this from happening, program your bot to crawl at varying speeds to more closely mimic the behavior of human scrapers.
6. Use a Headless Browser
Web servers can easily detect a request coming from a real browser. In turn, this helps them block IPs that are performing web scraping.
You can use a headless browser to solve this issue. As the name suggests, headless browsers are browsers without a graphical user interface (GUI). Headless browsers have browser tools that can help you resolve numerous JavaScript-related issues and perform browser automation tasks. Use a browser automation library to perform browser automation.
7. Scrape Data at Off-Peak Hours
Whenever possible, you should perform web scraping during off-peak hours with minimal server load. High-frequency requests during peak hours will slow down the target website and negatively impact its user experience.
8. Use CAPTCHA Solving Services
Websites commonly use CAPTCHA to block bot activity. Advanced scraping tools use CAPTCHA solving services to solve these puzzles.
9. Do Not Send Server Requests Often
Limit the number of server requests you’ll be sending. Similar to the rule about web scraping during off-peak hours, limiting your server requests will avoid overloading the target website’s server.
10. Don’t Follow the Same Crawl Pattern
Bots crawl quickly, efficiently, and predictably. Displaying a consistent pattern will cause anti-scraping technologies to identify a web scraping bot and block it. To avoid this, add random patterns to your crawl patterns to confuse the anti-scraping tech.
Types of Scraping Tools
- Web Scraper Browser Extension – This is a Chrome browser extension. Users can create a sitemap detailing how to crawl a website and what data to extract.
- Self-Built Web Scrapers – You have the option to build your own web scraper and fine-tune it to your specifications. This route requires advanced programming skills, including the ability to web scrape with Python.
- Cloud Web Scrapers – These web scrapers run on off-site servers, which the service provider usually provides. This avoids burdening your computer while the scraper is gathering and parsing data. Cloud-based solutions typically offer more advanced (albeit expensive) features.
- Web Scraping Software – These web scrapers contain advanced features, like IP rotation, JavaScript execution, proxy management, and anti-bot technology workarounds.
- User Interface Web Scrapers – These web scrapers have sophisticated user interfaces that allow the rendering of the website completely. The user simply has to click on the data they want to scrape.
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Web Scraping FAQ
Is Web Scraping Legal?
While web scraping isn’t illegal, you need to follow ethical rules. While extracting publicly-accessible data is legal, extracting restricted data from websites is illegal.
Examples of publicly-accessible data include blog posts, public social media profiles, and product pages on eCommerce websites. Restricted data include gated reports and research, course modules, and private social media profiles.
What Is a Web Scraping Tool?
Web scraping tools are software used to extract data from websites at scale. They’re also known as web data extraction and web harvesting tools. Web scraping tools vary in features and capabilities.
The most basic of these tools are browser extensions, with limited features and capabilities. Programmers also use programming languages like Python to code their own web scrapers. The most sophisticated and multi-featured web scrapers are web scraping software/APIs and cloud-based web scrapers.
Cloud-based web scrapers are ideal for those who want to scrape large numbers of URLs since the crawling and data extraction runs on off-site servers. Web scraping software/APIs can manage huge proxy networks, multiple web browsers, and CAPTCHA solving.
Why Use Python for Web Scraping?
Python has numerous features that make it ideal for web scraping. For starters, Python is simpler and cleaner to code as it lacks unnecessary symbols in its syntax. The syntax, as a whole, is also easier to understand since reading Python code is like reading sentences in English.
Python also has an extensive collection of libraries, including Pandas and Numpy. This gives programmers numerous resources to write fine-tuned web scraping code. It also generally takes less time to write and execute code in Python, even if the tasks they cover are large-scale and complex.
Lastly, the Python community is large and actively engaged with other members. If you ever encounter an issue or wish to crowdsource a solution, there’s likely someone qualified and eager to help you.