How to Crawl Data from a Website

5 min read

Data crawling is used for data extraction and refers to collecting data from either the worldwide web or from any document or file. The need for web data crawling has been on the rise in the past few years. The data crawled can be used for evaluation or prediction purposes under different circumstances, such as market analysis, price monitoring, lead generation, etc. Here, I’d like to introduce 3 ways to crawl data from a website, and the pros and cons of each approach.

Use Ready-to-Use Crawler Tools

Are non-coders excluded from web crawling? The answer is “no”. There are ready-to-use web crawler tools that are specifically designed for users who need data but know nothing about coding. 

1. Octoparse

With Octoparse, you can interact with any element on a webpage and design your own data extraction workflow. It allows in-depth customization of your own task to meet all your needs. Octoparse provides four editions of crawling service subscription plans – one Free Edition and three Paid Editions. The free plan is good enough for basic scraping/crawling needs.

If you switch your free edition to one of the paid editions, you can use Octoparse’s Cloud-based service and run your tasks on the Cloud Platform, enabling data crawling at a much higher speed and on a much larger scale. Plus, you can automate your data extraction and leave no trace using Octoparse’s anonymous proxy feature. That means your task will rotate through tons of different IPs, which will prevent you from being blocked by certain websites.

Octoparse also provides API to connect your system to your scraped data in real time. You can either import the Octoparse data into your own database or use the API to require access to your account’s data. After you finish configuring your task, you can export data into various formats, like CSV, Excel, HTML, TXT, and database (MySQL, SQL Server, and Oracle).

2. Mozenda

Mozenda is another user-friendly web data extractor. It has a point-and-click UI for users without any coding skills to use. Mozenda also takes the hassle out of automating and publishing extracted data. Tell Mozenda what data you want once, and then get it however frequently you need it. Plus, it allows advanced programming using REST API the user can connect directly with the Mozenda account. It provides the Cloud-based service and rotation of IPs as well.

3. ScrapeBox

SEO experts, online marketers, and even spammers should be very familiar with ScrapeBox with its very user-friendly UI. Users can easily harvest data from a website to grab emails, check page rank, and verify working proxies and RSS submission. By using thousands of rotating proxies, you will be able to sneak on the competitor’s site keywords, do research on .gov sites, harvest data, and comment without getting blocked or detected.

4. Google Web Scraper Plugin

If people just want to scrape data in a simple way, I suggest you choose the Google Web Scraper Plugin. It is a browser-based web scraper that works like Firefox’s Outwit Hub. You can download it as an extension and have it installed in your browser. You need to highlight the data fields you’d like to crawl, right-click and choose “Scrape similar…”. Anything that’s similar to what you highlighted will be rendered in a table ready for export, compatible with Google Docs. The latest version still had some bugs on spreadsheets. Even though it is easy to handle, it can’t scrape images or crawl data on a large scale.

Pros of Using Ready-to-Use Crawler Tools

  • Easy to pick up and non-coder friendly.
  • Applicable to all different kinds of websites.
  • Cost-efficient, no huge upfront charges, and many offer free editions.

Cons of Using Ready-to-Use Crawler Tools

  • Lack of customization options for complex data acquisition projects.
  • Each web scraping tool works a bit differently, so you’ll need to play around to find one that best suits your needs.
  • Just like any other skill, you’ll be required to spend time on it and work your way up in developing expertise with the tool​.

If you are still confused about how to get started with data crawling, the video below should shed some light on it. 

Use Website APIs

“An API in its simplest form is simply a bit of code that allows for two software programs to communicate with each other. It works to either permit or deny outside software to request information from the main program.” (explained in What is Application Programming Interface (API)?) An API enables companies to open up their applications’ data and functionality to external third-party developers, business partners, and internal departments within their companies. It allows services and products to communicate with each other and leverage each other’s data and functionality through a documented interface. 


Many large social media websites, like Facebook, Twitter, Instagram, and StackOverflow, provide APIs for users to access their data. Sometimes, you can choose the official APIs to get structured data. As the Facebook Graph API shows, you need to choose fields you make the query, then order data, do the URL Lookup, make requests, etc. To learn more, you can refer to https://developers.facebook.com/docs/graph-api/using-graph-api.

Pros of Using APIs to Crawl Data

  • High speed of exchanging requests and responses
  • Internet-based connectivity
  • 2-way communication with confirmations included within the reliable transaction sets, user-friendly experiences, evolving functionality

Cons of Using APIs to Crawl Data

  • High cost of implementing and providing API capabilities considering development times, ongoing maintenance requirements, and providing support
  • Unfriendly to non-programmers since APIs require extensive programming knowledge
  • Insecurity is caused by adding another potential attack layer to programs and websites.

Build a Web Crawler

Not all websites provide users with APIs. Certain websites refuse to provide any public APIs because of technical limits or other reasons. In such cases, some people may opt for RSS feeds, but I don’t suggest using them because they have a number limit. What I want to discuss here is how to build a crawler on our own to deal with this situation.

How does a crawler work? A crawler, put it another way, is a tool to generate a list of URLs that can be fed into your extractor. The crawler can be defined as a tool to find the URLs. Give them a webpage to start with, and they will follow all these links on that page. This process then keeps going on in a loop.

Read about:

Believe It Or Not, PHP Is Everywhere

The Best Programming Languages for Web Crawler: PHP, Python, or Node.js?

How to Build a Crawler to Extract Web Data without Coding Skills in 10 Mins

How to build a web crawler?

Then, we can proceed with building our own crawler. It’s known that Python is an open-source programming language, and you can find many useful functional libraries. Here, I suggest the BeautifulSoup (Python Library) for the reason that it is easier to work with and possesses many intuitive characters. More exactly, I will utilize two Python modules to crawl the data.

BeautifulSoup does not fetch the web page for us. That’s why I use urllib2 to combine with the BeautifulSoup library. Then, we need to deal with HTML tags to find all the links within the page’s <a> tags and the right table. After that, iterate through each row (tr) and then assign each element of tr (td) to a variable and append it to a list. Let’s first look at the HTML structure of the table (I am not going to extract information for table heading <the>).

Pros of Building Your Own Crawler

  • The customized crawler with the whole process within your control
  • Proxies available for preventing the crawler from being blocked by some websites
  • Friendly to people with coding skills

Cons of Building Your Own Crawler

  • Time-consuming to crawl a website on your own by programming
  • Unfriendly to people without any coding skills (Alternatively, the no-coders can hire a freelance web scraping developer. But both learning to program and hiring some professional people are approaches adding overheads to the data collection operations)

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