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The Ultimate Guide to Efficient Large-Scale Web Scraping [2024]

Web scraping, the process of extracting and organizing data from websites, has become an essential skill for data-driven businesses. However, the volume and complexity of data involved in large-scale web scraping pose technical and ethical challenges, necessitating a comprehensive understanding of the tools, techniques, and best practices for achieving success.

In this guide, we will explain how to scrape data at a large-scale efficiently and ethically. We will explore the challenges of large-scale data extraction and provide you with best practices to overcome these hurdles.

What is large-scale web scraping?

Large-scale web scraping is the process of extracting data from websites on a vast scale. Although users  can perform  it manually, the term typically refers to an automated process implemented by web crawlers or scrapers.

How to scrape websites at large-scale

Scraping websites at a large-scale effectively requires the combination of a well-planned strategy and automated tools to handle the challenges that arise. There are typically two methods for scraping data at scale:

1. Scraping hundreds of pages from a large website:

Scraping data from many pages on a single large website, such as an e-commerce site, presents its difficulties. For example, large websites typically have complex pagination and navigation systems. To scrape data from large websites, you need a web scraper to navigate the site’s structure and handle pagination effectively.

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Smartproxy offers no-code scraper and web scraping APIs designed to extract large amounts of data from multiple websites or webpages. The web scraping services allow users to gather data from JavaScript, AJAX, or other dynamic websites.

2. Scraping data from thousands of different small websites:

Scraping data from different websites involves extracting  data from numerous smaller websites simultaneously. This type of large-scale web scraping makes it challenging to handle diverse website structures when scraping data.

  • Identify the target websites or web pages: Determine the websites or web pages from which you want to extract data.
  • Making connection requests: The method of sending HTTP requests varies depending on the web scraping method. You can send requests to the target website using an automated tool or an in-house web crawler. Most off-the-shelf web scrapers have built-in anti-scraping technologies to overcome scraping measures such as CAPTCHAs and IP bans. If you extract data from web pages using an in-house scraper, you will need to integrate proxy services and IP rotation mechanisms into your scraper to scrape data effectively at a large-scale.
  • Parse HTML content: Parse the HTML content of the web page to locate the desired data elements. You can use the Beautiful Soup library to parse the HTML source of the web page you are scraping.
  • Extract desired data: Once you have parsed the source of the target web page, you can extract the desired data from the HTML content.
  • Clean and process extracted data: After scraping all the required data, you must ensure that the data is accurate, consistent, and ready for analysis. However, cleaning large datasets is challenging and time-consuming due to the volume of data and heterogeneity. You may need powerful tools and various techniques, such as parallel processing, to handle large datasets.
  • Save data: Pre-built web scrapers automatically download and save scraped and processed data in different formats. If you have scraped data using a web scraping library such as Scrapy, you have several storage options to save the collected data.
    For instance, you can store data in databases such as SQLite, PostgreSQL, etc. For SQLite, you can use Python’s built-in “sqlite3” module. If you want to store data in a text file format such as CSV, you can use Python’s built-in “CSV ” module.

Apify is a cloud-based platform that offers tools to automate web scraping and web automation tasks. The platform provides pre-built scrapers for popular websites like Amazon, eBay, and Instagram, handling large-scale data scraping tasks efficiently. Users can also develop their own actors using Apify SDK.

What are the challenges of web scraping at a large-scale?

Large-scale web scraping poses numerous challenges due to the complexity of handling extensive data volumes and the technical components involved. Here are some of the most common challenges of large-scale scraping:

  1. Dynamic websites: Dynamic websites, as opposed to static websites, use JavaScript to load or display content, making traditional web scraping methods challenging to collect data from. Most dynamic websites require user interactions, such as clicking buttons or filling out forms. Your scraper must be able to simulate these interactions to access data.
  2. Rate limiting: Websites employ rate limiting to control the number of requests a client can make within a specific period. This protects API from malicious bots and prevents their data from being abused or misused.
  3. Data accuracy: It can be difficult to ensure data accuracy, especially when working with large datasets. For instance, large datasets collected from multiple sources can result in data inconsistencies. Inspecting new data manually, especially in large datasets, can be impractical and tedious. You can employ automated metrics to validate and inspect data, such as leveraging machine learning algorithms or developing scripts.
  4. Anti-scraping measures: Many websites employ anti-scraping mechanisms, such as CAPTCHAs, JavaScript challenges, and IP blocks, to prevent or restrict web scraping activities.

Bright Data’s Web Unlocker enables developers to collect data while circumventing website restrictions such as CAPTCHAs and IP bans. To avoid bot detection systems such as browser fingerprinting, Web Unlocker employs either a datacenter or residential IP address.

Bright Data's Web Unlocker enables developers to scrape data while circumventing anti-scraping measures.
Source: Bright Data

How to perform large-scale web scraping effectively

We have compiled the following key guidelines to help you overcome large-scale web scraping challenges, ensuring efficient and legally compliant data extraction. It is important to use these best practices responsibly and in accordance with website terms of service.

  1. Headless browsers: Headless browsers allow users to extract the data they require from dynamic websites. When scraping dynamic sites,  you can use headless browsers to simulate user interactions, such as mouse movements and clicks.
  2. Proxies and IP rotation: Most web scraping libraries and tools provide options for using proxy servers. Pre-built web scrapers often include built-in integration with proxy services to help users avoid being blocked by target websites.
    For example, rotating proxies allow web scrapers to bypass the rate-limiting and make more requests without being flagged as suspicious. We advise employing residential IPs widely known for their reliability and speed.

Oxylabs rotating ISP proxies  are combinations of datacenter and residential proxies. Since ISP proxies are associated with Internet Service Providers, they appear to be residential IPs. This makes them suitable for web scraping projects.  

  1. Web browser automation: Web automation tools like Selenium and Puppeteer enable you to mimic human activities and interact with websites in the same way that humans do. This can be useful for extracting large amounts of data from dynamic websites without manually navigating the site.
  2. Distributed computing techniques: A distributed web scraping architecture enables more efficient large-scale web scraping by dividing and spreading  web scraping tasks across multiple machines. You can build your distributed scraper in any language based on your familiarity to overcome challenges such as rate limiting and handling dynamic content.

Further reading

For guidance to choose the right tool, check out data-driven list of web scrapers, and reach out to us:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collections and applications of web data.

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