Web Scraping vs. Web Crawling

Web scraping and web crawling are methods used to gather data from different sources on the internet. Although these terms are often used interchangeably, there are distinct differences. Both techniques are essential for making decisions based on data in today’s digital economy.

This article offers a comprehensive overview of web scraping vs web crawling. You will understand how web scraping and web crawling work, their strengths, weaknesses, and applications. It’s a useful resource for anyone interested in data mining who wants to distinguish between these concepts.

What is Web Scraping?

Web scraping is the process of gathering and examining raw data from the internet. Web data collection can be done manually (copying and pasting information from websites) or automatically using web scraping tools.

Web scraping aims to convert specific website content into a structured format, such as tables, JSON, databases, and XML representations.

What is Web Crawling?

Web crawling is an automated method of accessing publicly available websites and gathering the content from those websites.
A web crawler, web spider, or crawler is a program that searches and automatically indexes web content and other data over the web. Web crawlers scan web pages to understand every website page to retrieve, update, and index information when users perform search queries.
The goal of a crawler is to understand the content of a website. This allows users to extract information on one or more pages as needed.

Importance of Web Scraping and Web Crawling in Data Extraction

To grasp the relevance of web scraping and web crawling, we should understand what each of these entails.

Understanding Web Scraping

The web scraping process can be summarized as follows:

1. Send a request to the desired website.
2. Next, retrieve the information in HTML format.
3. Then, analyze and extract the relevant answers by parsing the textual code.
4. Finally, save the extracted content in a preferred format such as JSON, XML, Excel, CSV, or a database. This enables easy retrieval and future use of the stored data.

Web Scraping Techniques

Web scraping isn’t as simple as the aforementioned four steps. Still, it isn’t too complicated either- with the following techniques, you can scrape the web without many hurdles:

1. HTML Parsing

This technique involves parsing the HTML structure of a web page using libraries like BeautifulSoup or LXML. It allows you to navigate the document’s elements and extract data based on their tags, attributes, or CSS selectors.

2. Headless Browsers

Headless browsers, like Puppeteer or Playwright, allow you to automate browser actions programmatically. They simulate user interactions and rendering of web pages, enabling the scraping of dynamic websites that heavily rely on JavaScript for content generation.

3. API Access

Some websites provide APIs (Application Programming Interfaces) that allow developers to access and retrieve data in a structured manner. Instead of scraping the HTML content, you can directly query the API endpoints to retrieve the desired data, usually in JSON or XML format.

4. Manual Navigation

This involves manually navigating through a website and collecting web data along the way. If the desired data is dispersed across multiple pages or is not easily accessible through automated scraping techniques, manual navigation may be preferable.

Benefits of web scraping

1. Data Extraction Efficiency

Web scraping automates data collection from the web, significantly reducing the time required compared to manual methods. It quickly traverses multiple web pages, collecting vast amounts of data in a fraction of the time.

2. Cost Savings

By automating data extraction, web scraping eliminates manual data entry, reducing the costs associated with labor and potential human error. This cost-saving advantage allows for the reallocation of resources to other essential business operations.

3. High Insightfulness

Web scraping collects diverse data from a website. This data can help businesses understand competitors, identify market trends, and track customer sentiment, aiding strategic decision-making processes.

4. Real-time Data

Web scraping can provide real-time data, ensuring the most recent information is readily available. This feature is critical in sectors such as finance, where up-to-date data is essential.

5. Data Accuracy

Web scraping automates data extraction, reducing the risk of human error and increasing accuracy. Advanced tools can also manage complex data structures and formats, ensuring high reliability.

Legal and Ethical Considerations in Web Scraping

Gathering or scraping publicly available information is not illegal. Generally, Web scraping is legal if you are scraping publicly available data.
Please refer to our Legal Page to learn more about the legality of web scraping.
Legal Information – ScrapeHero

Challenges in web scraping

• HTML fingerprint – The filtering process starts with a granular inspection of HTML headers. These can indicate whether a visitor is a human or a bot and malicious or safe. Header signatures are compared against a constantly updated database of over 10 million known variants.

• IP reputation – We collect IP data from all attacks against our clients. Visits from IP addresses having a history of being used in assaults are treated with suspicion and are more likely to be scrutinized further.

• Progressive challenges – We use a set of challenges, including cookie support and JavaScript execution, to filter out bots and minimize false positives. As a last resort, a CAPTCHA challenge can weed out bots attempting to pass themselves off as humans.

• Behavior analysis – Tracking the ways visitors interact with a website can reveal abnormal behavioral patterns, such as a suspiciously aggressive rate of requests and illogical browsing patterns. This helps identify bots that pose as human visitors.

Exploring Web Crawling

The web crawling process can be broken down into the following steps:

1. Fetch the URLs provided from the crawl frontier.
2. Proceed to explore each page that is linked to those URLs.
3. Evaluate and categorize all web pages encountered.
4. Store the collected URL data in the database by indexing it.

Applications of Web Crawling

Web crawling involves using automated scripts or programs to index data from web pages. Web crawling applications include:

Search Engine Indexing (Google Bot)

Web crawling is essential for search engine indexing. For instance, Google uses its web crawler, Google Bot, to scan and index billions of web pages. This indexed data is displayed when users enter search queries on Google.

Website Health Monitoring

Web crawling can help maintain the health of websites. Web crawlers scan websites to identify issues such as broken links or coding errors, helping to ensure optimal website performance and user experience.

Content Aggregation

Content aggregation sites use web crawling to collect information from various sources into a single location. For example, news aggregation sites use web crawling to pull articles from different publications, creating a single platform for diverse content.

Competitive Analysis

Web crawling can be used for competitive analysis by extracting data from competitors’ websites. This could include information about pricing, new product launches, and marketing strategies, providing valuable insights for strategic decision-making.

Crawling Policies

The actions of a Web crawler are determined by a set of policies working together:

• A Selection Policy that states the pages to download.
  The selection policy determines which pages to download or crawl. There are different approaches to page selection, depending on the specific requirements of the crawling task. Some common selection policies include
  • Breadth-First Search
  • Depth-First Search
  • Focused Crawling

• A Re-Visit Policy that states when to check for changes made to the pages.
  The re-visit policy determines when to check for changes to the previously crawled pages. Crawlers can revisit web pages periodically to detect updates, new content, or changes in existing content. The choice of re-visit policy depends on the frequency of updates on the target websites and the desired freshness of the data.
• A Politeness Policy that states how to avoid overloading Websites.
  The politeness policy governs how crawlers interact with websites to avoid overloading and respecting their resource limitations. Crawlers should be designed to be polite and avoid causing disruption or excessive load on web servers.
• A Parallelization Policy that states how to coordinate distributed web crawlers.
  The parallelization policy addresses the coordination and management of distributed web crawlers when multiple crawlers are deployed to accelerate the crawling process. It involves efficient workload distribution and coordination to avoid duplicate crawling, manage shared resources, and ensure comprehensive coverage.

Challenges of Web Crawling

1. Database freshness

A website’s content is updated regularly. Dynamic web pages, for example, change their content based on the activities and behaviors of visitors. This means the website’s source code does not remain the same after you crawl the website. In order to give users the latest information, web crawlers need to crawl web pages more frequently.

2. Crawler traps

Websites employ different techniques, such as crawler traps, to prevent web crawlers from accessing and crawling certain web pages. A crawler trap, or spider trap, causes a web crawler to make infinite requests and become trapped in a vicious crawling circle. Websites may also unintentionally create crawler traps. When a crawler encounters a crawler trap, it enters something like an infinite loop that wastes the crawler’s resources.

3. Network Bandwidth

Downloading a large number of irrelevant web pages, utilizing a distributed web crawler, or recrawling many web pages all result in a high rate of network capacity consumption.

4. Duplicate pages

Web crawler bots mostly crawl all duplicate content on the web; however, only one version of a page is indexed. Duplicate content makes it difficult for search engine bots to determine which version of duplicate content to index and rank. When Googlebot discovers a group of identical web pages in search results, it indexes and selects only one of these pages to display in response to a user’s search query.

Tools for Web Crawling and Scraping

Various tools and software are readily available for web scraping and web crawling.

Refer to the following articles to find the tool best aligned with your requirements:
Best Web Crawling Tools and Frameworks in 2023
10 Best Web Scraping Tools and Software in 2023

If you’re looking for a service that can fully automate your web scraping and web crawling needs, visit our Web Scraping Service and Web Crawling Service.

Web Scraping vs. Web Crawling

Give a simple table example given below

In brief, web scraping is about extracting the data from one or more websites. While crawling is about finding or discovering URLs or links on the web.

When it comes to web data extraction, it’s usually necessary to use a combination of crawling and scraping. To start, you crawl the URLs to discover them and download the HTML files. From there, you can scrape the data you need from those files. This means that you’re extracting data and taking some action with it, such as storing it in a database or further processing.

Differences in Web Scraping vs Web Crawling

The difference between web scraping and web crawling are based on two key aspects:

The Purpose

When it comes to web scraping, the focus is on the data you want to gather from specific websites. With scraping, you’re usually aware of the target websites, even if you don’t know the exact page URLs. On the other hand, crawling is all about discovering the URLs themselves, which you may not even know in advance. Search engines, for instance, crawl the web to find pages to index and display in search results.

The Output

Web crawling outputs a simple list of URLs, while web scraping involves extracting multiple data fields, typically anywhere from 5 to 20 or more. URLs may be included, but the focus is on gathering data from various fields displayed on the website, such as product names, prices, or other relevant information for the business use case.

Similarities in Web Scraping vs Web Crawling

In this discussion of web crawling vs web scraping,

• Making HTTP requests is the common way for web crawling and web scraping to access data.
• Both processes are automated, which leads to more accurate data retrieval.
• Web crawlers and scrapers risk being blocked through IP clamp down or other means.
• Despite differences in their workflow, web crawling and web scraping both involve downloading data from the web.

Wrapping Up: Web Crawling vs Web Scraping

Web scraping and web crawling are key methods in data extraction and indexing from the web. Web scraping efficiently pulls specific data, aiding in market research and competitive analysis tasks.

On the other hand, web crawling is essential for indexing web pages, supporting search engine functions, and website health monitoring.

The future holds even more importance for these techniques. As online data grows, efficient and reliable methods like web scraping and web crawling become more necessary.

Rather than finding an answer to web scraping vs web crawling, the goal of this article is to illustrate how both web scraping and web crawling play vital roles in understanding and navigating the digital landscape.

Despite their differences, they complement each other, helping us make the most out of the vast amount of online data. As we move further into the digital era, the understanding and application of these methods will continue to be crucial.