Web Crawling vs Web Scraping

Data is essential for decision-making, innovation, security, and more. When data is extracted, it involves collecting and retrieving information from multiple sources for analysis or storage.

This data can be leveraged for research, AI and machine learning, stock market analysis, cybersecurity, and other applications.

Extracted data can be obtained through various ways, with web crawling and web scraping being two of the most common. But what purposes do these methods serve? This article will compare them, explore their function and break down their differences.

Web crawling involves using automated bots, also known as web crawlers or spiders, to browse the internet, retrieve content, and index web pages. These bots help search engines systematically organize and categorize content data.

When you search for a website like Amazon on Google, and browse its products, you're engaging in web browsing which is made possible by crawlers that scan and organize website content for search engines.

A web crawler has three main components, namely;

Frontier: This process begins with a list of unvisited URLs, known as seed URLs - a collection of initial web addresses. The crawler proceeds to retrieve a URL from this list, then fetches the corresponding webpage, and scans it for additional links. These newly discovered links would then be added to a queue for further crawling, allowing the process to continue consistently.

Page Downloader: This component retrieves webpages from the internet that correspond with the URL retrieved from the frontier. It relies on an HTTP client to send requests, and read responses. To improve efficiency, a timeout period is set to prevent delays caused by reading of unnecessarily large files, or slow server response.

Web Repository: This stores and manages a large volume of data, in this case, HTML pages. It also has a storage manager that manages an up-to-date version of every page retrieved by the crawler.

Web crawlers start from seed URLs provided by various sources, for instance, site owners submit URLs to search engines, domain registration lists, and so on. They visit each URL (excluding direct media storage which are saved based on references and metadata related to them), and follow links to systematically index web pages.

The collected data is stored in a central repository, where it is indexed for retrieval. Crawlers also revisit pages periodically for updates. Before crawling, bots check the robots.txt file in the sites root directory to determine which pages should be indexed or not. Here’s a more comprehensive explanation of how web crawling works.

Web crawler impacts the web in many ways, common examples include;

Search Engine Optimization:

Web crawlers help search engines index websites, improving their visibility in search results. If the site is not crawlable due to errors or broken links it may rank lower or not appear at all. Regular crawling ensures that updates and changes are reflected in search ranking. There are best practices for making sure your website is crawlable, including;

• Creating a well-structured website that helps crawlers index content effectively.

• Using robots.txt file to guide crawlers on important pages to crawl, preventing them from crawling unnecessary ones.

• Ensuring content quality is high and contains relevant information.

• The use of external links to ensure important pages are connected and accessible to crawlers.

Monitoring Website Changes and Updates:

The search engine schedules crawlers to revisit websites, and the crawler checks for new updates, new pages, deleted pages, etc, and if there are updates found, the search engine updates its index to reflect the changes. Sites that are updated frequently are crawled more often compared to static websites.

Data Collection:

Web crawlers gather large amounts of data from websites that can be used for market research or analysis to track competitors, and analyse trends.

There are four main types of web crawlers, including;

Focused Web Crawler: A focused web crawler retrieves pages that are related to specific topics, collecting documents that are relevant to it. It determines how a page is relevant to a given topic, and decides whether to proceed. This approach is more efficient in terms of hardware and network resources because it reduces the amount of network traffic and unnecessary downloads.

Incremental Crawler: An incremental crawler refreshes an existing collection of pages by revisiting them frequently based on how often the pages are updated. It replaces less important pages with more important ones, ensuring that data is always up to date. This approach is different from a traditional crawler which periodically replaces old documents with newly downloaded ones. Incremental crawler provides users with valuable data which optimizes network bandwidth usage.

Distributed crawler: This is a distributed technique where many crawlers work together to distribute the process of web crawling to have the most coverage of the web.

They operate across geographically distributed nodes, with a central server that manages the synchronization and communication between them. Some distributed crawlers use page rank or other algorithms to increase the efficiency and search quality, making them great for large-scale web indexing.

Parallel Crawler: This type of crawler is where the bot runs multiple processes in parallel or simultaneously to increase efficiency. These crawlers are known as C-procs (Crawling Processes) and they can operate on a local network or distributed across distant locations. They rely on page freshness and page selection to optimize crawling. Parallel crawlers can download large volumes of content in good time.

A list of popular web crawlers includes;

• Googlebot: Web crawler for Google search engine

• Bingbot: Web crawler for Microsoft search engine

• YandexBot: Web crawler for Yandex search engine

• DuckDuckBot: Web crawler for DuckDuckGo search engine

• Slurp: Web crawler for Yahoo

• BaiduSpider: Web crawler for Baidu

• AmazonBot: Web crawler for Amazon

• ExaBot: Web crawler for Exalead search engine

If a website contains valuable data for a purpose such as market research, lead generation, data analysis, etc, manually copying and pasting this information may be impractical - especially when dealing with large volumes.

This is where web scraping comes in. Web scraping, also called web harvesting or data extraction, is an automatic process that involves the use of bots to extract data from websites.

Web scraping can be done manually or automatically using a scraper, and there are different types of scraping tools with specific features and functionalities. Also, there are certain rules to follow when scraping a website, for instance, it is considered illegal to scrape a website that isn’t publicly available.

In a nutshell, a URL is loaded into a scraper - which is a tool that extracts data from websites. The scraper then loads the HTML code of the specific page(s) and extracts all or specific data required by the user.

Before running the scraper, the user selects or defines the necessary data they want to retrieve, assuming they are looking for specific information rather than extracting all the website content.

Typically, a web scraper returns data in an Excel sheet or CSV file. More advanced scrapers with enhanced features support JSON format, which can be used for an API. Some scrapers also provide a more dynamic data extraction, including JavaScript, HTML, and CSS data.

There are different reasons why you may need to scrape a web page(s), which include;

• When you need data from a web page that doesn’t provide an API

• When the data you need from a webpage is too much to copy and paste manually

• When the data is constantly being updated, copying and pasting would be inefficient. A web scraper automates this process by continuously extracting required data.

A few limitations or challenges of web scraping include;

• Data restrictions - some websites are public but may still deny you access to their data

• Ethical concerns - many websites do not allow automated scraping, as well, scraping copyrighted or personal data can lead to legal battles.

• Your IP address can be blocked when making frequent requests to scrape a website

• Scraping large volumes of data consumes bandwidth and requires high computational power.

• Websites use Anti-Scraping measures like CAPTCHAs to prevent bots from accessing website data

There are different categories of web scrapers depending on the needs of the user, these include;

Browser-Based Scrapers: These scrapers run within a browser, like Chrome, or Firefox. They run locally, meaning your data is stored and processed locally on your device, ensuring better security and privacy.

However, since they operate from your local IP address, they are great for simple operations. They are user-friendly and are accessible for non-technical users. Examples include Instant Data Scraper, Web Scraper, and Scraper API.

Cloud-Based Scraper: These scrapers operate from a separate cloud server, keeping your local IP secure from being blocked. They are more expensive but ideal for high-volume scraping operations. Examples include Scrapy Cloud, and ParseHub.

Hybrid Scraper: These scrapers combine browser and cloud-based scraping features, offering more efficiency based on your scraping needs. Examples include Apify and PhantomBuster.

As the world progressively adopts AI, it integrates with traditional web scraping tools to automate the extraction of data from websites using AI-based methods. Traditional web scraping relies on predefined selectors that isolate specific data, while AI web scraping employs self-adjusting algorithms capable of handling dynamic websites.

This addresses the limitations of manual, code-based scraping techniques. AI-powered web scraper is designed to navigate through web pages, extract relevant data, and adapt to changes in website layout with limited human intervention.

Benefits of AI Web Scraper include;

• Great Adaptability - adjust to changes in website structure automatically

• Efficient Data storage - Organizes extracted data in various formats such as JSON, Excel, CSV

• Capable of extracting different data types including text, images, and videos.

• Automatically collect data without manual input

Examples of AI Web Scrapers include;

• Bardeen AI
• Web Scraper IO
• ParseHub
• Diffbot
• OctoParse
• Instant Data Scrapper

Highlighting key differences between web crawling and web scraping:

Web crawling and scraping are data extraction methods that are useful for various reasons. Web-crawlers discover and index for search engines while scrapers extract data for specific purposes like market research, price comparison, lead generation, and more. These tools help users navigate and extract information from websites efficiently.