The term Big Data refers to tools, approaches, and techniques. All these are useful for collecting and then processing structured or unstructured data. Then you use it to solve specific problems or achieve specific goals. The term, introduced by Clifford Lynch in 2008, described the phenomenon. It was first caused by the rapid growth of the global volume of digital information. It was due to the emergence of tech capabilities for their storage and analysis: web scraping vs web crawling.

A little earlier, it was possible to get it only manually, going to the pages of sites. But this approach took time, effort, and, importantly, money. So, the automation of data collection became a matter of time. A few years later, marketers, SEO specialists, and analysts received several proxies useful for SEO. So they can work with Big Data – web scrapers and crawlers. Despite similar tasks, there are a lot of differences between scraping and crawling. Read the article below to learn about web scraping vs web crawling.

Crawler vs Scraper: Understanding the Basics

Web crawling or indexing is usually used to index information on a page using bots (crawlers). Scanning is essentially what search engines do. It is about crawling a page and indexing it. When a bot scans a site, it crawls every page and every link down to the last line of the site, looking for any information.  

Search bots use:

• search engines like Google, Bing, Yahoo;
• statistical agencies;
• major online aggregators. 

The crawling process usually captures general information. But web scraping captures specific parts of a data set.

Data scraping is like web scanning in identifying and finding targeted data on web pages. The key difference is that in scraping, we know the exact identifier of the dataset. It is the structure of the HTML element for the captured web pages from which the extracted data.  

Web scraping is an automated way of extracting specific datasets using bots. They are also known as “parsers.” Once you have collected the necessary information, you can use it to:

• compare;
• validate;
• analyze. 

These tasks are often based on the needs and goals of a given business. Below you can discover more difference between web scraping and web crawler.

Purpose

Crawlers are primarily employed by search engines like Google, Bing, and Yahoo. Their primary function is to:

• traverse the internet;
• index web pages;
• gather information to create searchable databases. 

Scrapers work with targeted data extraction from web pages. Their purpose varies widely and includes tasks like:

• price monitoring;
• content aggregation;
• lead generation, and more.

Frequency

Search engine crawlers revisit websites periodically to update their index with:

• new content;
• changes. 

Scrapers can run as often as needed, depending on the user’s requirements. They collect the latest data from specific sources.

Selection

Web scraping vs web crawling select and extract specific data elements from a webpage, such as:

• product prices;
• contact information;
• news headlines;
• structured information.

Crawlers follow links from one web page to another. They systematically explore the web’s interconnected structure. They analyze and catalog content. It allows users to find relevant information through search queries.

Depth

Crawlers can delve deeply into websites. They follow links to reach pages nested several levels deep. They aim to create a comprehensive index of the internet. Scrapers focus on individual web pages or a set of predetermined web pages. They are not concerned with indexing or navigating the entire web.

Key Differences between Web Crawling and Web Scraping

A difference between web scraping and web crawling is that a crawler can move between pages without a clearly defined purpose or task. They examine the project itself by dozens or hundreds of criteria. It is usually used by search engines and refined through machine learning. You need to take the scanning results into account in constructing the top of the issue. This is an essential thing to know when discovering the differences. 

A scraper is a program or script that extracts certain data specified by the user. Unlike the “spider”, it searches for specific information on a site or page.

It is worth noting that a web crawler creates and saves a copy of a page. While a web scraper extracts data to create or populate a new site. In addition, standalone bots work with the entire resource content:

• texts;
• images;
• media content;
• files. 

A scraper extracts only textual information. It puts the information into a file format convenient for further work. Below, you can read more differences in crawler vs scraper.

User-Agent

• Web Crawling. Crawlers often identify themselves with a standard User-Agent header when accessing websites.
• Web Scraping. Scrapers may use custom User-Agent headers to mimic human browsing behavior. They use it to avoid being detected as bots.

Ethical and Legal Considerations

• Web Crawling. Ethical and legal guidelines for web crawling are well-defined. And we expect crawlers to adhere to a website’s “robots.txt” file and terms of service.
• Web Scraping. Web scraping raises more ethical and legal concerns involving data extraction. Scrapers should respect website terms of service. They must consider the legality and consent issues associated with data collection.

Tools and Libraries

• Web Crawling. Web crawling vs web scraping is often performed by dedicated search engine bots like Googlebot or Bingbot. It utilizes proprietary algorithms.
• Web Scraping. Scraping is accomplished using various programming languages and libraries. It can be Python’s BeautifulSoup, Scrapy, or JavaScript’s Puppeteer. 

Web Crawling and Scraping Using Python

As a high-level interpreted language, Python 3 is one of the easiest languages to read and write. Because its syntax is similar to English. Fortunately for us, Python is much easier to learn than English. Programming in Python is also a great choice for anyone who wants to pursue:

• data science;
• artificial intelligence;
• machine learning;
• web applications;
• image processing, or operating systems. 

You can use the step-by-step instructions for web crawling and scraping using Python that follow:

• Step 1. Select the URLs you want to clean up
• Step 2. Find the HTML content you want to clean up. Once you have selected your URLs, you need to figure out under which HTML tags or attributes the data you want will be located. At this point, you need to check the source of your web page (or open your developer toolbar).
• Step 3. Select tools and libraries. We recommend using the Selenium and Beautiful Soup 4 (BS4) libraries in addition to the module for this task.
• Step 4. Create your parser in Python. This is the last and final step. After completing it, you will get the finished result.

 

Benefits of Web Crawling and Scraping Using Python

We can consider the following things to be benefits:

• Versatility. Python offers a wide range of libraries and frameworks like:

• BeautifulSoup;
• Scrapy;
• Requests.

So it becomes a versatile option for both web crawling and scraping tasks.

• Ease of Use. Python’s straightforward syntax and readability simplify development. Si it is accessible for beginners and experienced programmers.
• Rich Ecosystem. Python has a vibrant and active community. It results in abundant documentation and tutorials. So you can get support for web crawling and scraping projects.
• Data Processing.  It is a very important thing while learning web crawling and scraping using Python. Python’s powerful data processing libraries enable efficient analysis and manipulation of scraped data.
• Cross-Platform Compatibility. Python is cross-platform, meaning you can run your scripts on various operating systems. So it enhances flexibility and accessibility.
• Integration. Python can easily integrate with databases, APIs, and other tools. So it streamlines the data pipeline for comprehensive analysis.

Conclusion

Bright Data offers many advanced solutions for those who want to perform web scraping vs web crawling. Web Unlocker uses machine learning algorithms to constantly find the best or fastest way to collect targeted open-source data. In contrast, the Web Parser IDE is a fully automated, zero-code parser that delivers data straight to your inbox. Consider using Proxy-Cheap’s affordable and reliable proxy services. So you get seamless and efficient web scraping. Discover all our propositions and plan to make your online experience better. So feel free to contact us if you have any questions.

 

FAQs 

Can I use web scraping to index websites like a crawler?

Web scraping primarily focuses on extracting specific data from web pages rather than indexing entire websites, which is the primary function of web crawlers. However, it may not provide dedicated web crawlers’ comprehensive coverage and indexing capabilities.

Can I use a web crawler to gather specific data like a scraper?

You can customize them to gather specific data to some extent. However, it’s more practical to employ web scraping techniques and libraries like BeautifulSoup or Scrapy for efficient and precise data extraction from web pages.

Which Python library is best for web scraping?

The choice of the best Python library for web scraping depends on your project’s complexity and your familiarity with the tools. BeautifulSoup is suitable for parsing HTML and XML documents, making it an excellent choice for simpler scraping tasks.

Is web scraping faster than web crawling?

The speed of web scraping versus web crawling is influenced by several factors, including the size and complexity of the websites being processed and the efficiency of your scripts.