Web scraping may appear quite easy when you are starting out. Several open-source libraries, frameworks, visual scraping applications, and data collection tools make the scraping process somewhat easier. However, when scraping data at scale, things tend to get really tricky.
If you want to scrape the web at scale and reliably target location-specific data, you must have robust systems and web services in place. Without well-managed tools and services, your team will spend the majority of its time attempting to organize them and will be unable to scrape data at scale effectively.
In this post, we will discuss scraping at scale, its use cases, and some common scaling problems and their solutions.
So, let’s take a look.
What Does Collecting Web Data at Scale Means?
Web scraping is a process of fetching and extracting data from target websites. This technique helps companies extract data from websites and store it for further processing and analysis.
Collecting web data at scale means sending several parallel requests to a website to obtain as much data as possible in a limited time frame.
There is another definition according to which data scraping at scale means the attempt to extract a large amount of data from multiple sources at once. In both cases, the method involves regular data collection in vast numbers.
Use Cases For Data Scraping At Scale
Data scraping at scale can benefit businesses in many ways, and here are some of them:
- Price Intelligence – Scraping data at scale helps companies stay on top of the latest changes in prices and product information, offering a detailed overview of the market.
- Competitor Analysis – A large number of data obtained on your competitors, the type of their products/services, and their selling techniques equip you to market your business at a professional level.
- Lead Generation – Data scraping for lead generation helps businesses find qualified leads from several sources at scale. It helps compile important data for businesses to reach out to their potential customers for marketing reasons.
Common Problems Of Web Scraping At Scale
At times, scraping at scale can be problematic. In this section, we’ll discuss some common difficulties of web scraping at scale and ways to overcome them.
CAPTCHAs
CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a good approach to keep bots away from a site. Many website hosts use CAPTCHA prompts.
So, to easily scrape data from such sites, we need a system to solve CAPTCHAs. Many software options are available that can solve them and act as middleware between your target site and the scraper.
Anti-scraping Technologies
Today, virtually every site host wants to prevent their data from getting fetched. Anti-scraping technologies are helping them with this. For instance, regularly sending requests to a specific website from the same IP address can get your IP blocked.
However, there are some methods that you can follow to bypass these anti-scraping methods. You can try using proxies to hide your original IP. Many proxy services can rotate IPs before each request, and it is easy to add support for them in the code.
Structure Changes
Data scraping has a lot to do with UI and its structure. In case the target site gets some adjustments, our scraper may crash totally or retrieve inaccurate/irrelevant data. It is a common scenario, which makes it more difficult to maintain scrapers than write them.
To overcome this case, we can create test cases for the retrieval logic and execute them daily, either manually or from tools, to track if the target site has changed or not.
Honeypot Traps
There are some sites having honeypot traps on the pages to detect bots. These traps can’t be easily noticed as many of the links are blended with the background color, or the CSS display property is set to None.
To overcome this difficulty, extensive coding efforts are required on both the server and the bot sides.
JavaScript-based Dynamic Content
Data collection gets difficult for websites that rely on JavaScript and Ajax to present dynamic content. Now, many libraries or frameworks will only work or retrieve what it gets in the HTML document.
Ajax calls are executed at runtime, so it can’t gather that. One way of handling this is by rendering the page in a headless browser, which allows running Chrome in a server environment.
How To Scrape Effectively?
As mentioned in the beginning, when it comes to web scraping at scale, there are many things to consider. Of course, you can certainly set up the right workflow and circumvent the common problems we’ve discussed through research as well as trial and error.
This is a great option for those who have a good in-house technical team eager to learn and gain expertise. However, some companies simply do not have the time or resources for that.
In these cases, it’s best to turn to companies that have the know-how as well as the tools. And there are many out there.
A recent trend in web scraping is shifting from HTML scraping to using a scraper API. API specifies possible interactions between two or more computer programs.
The integration of API to web scraping has been great as it aids programmers in circumventing common web scraping problems much easier. What is more, it’s usually offered as a stand-alone tool for the end-user, making scraping more accessible.
Multiple companies specializing in data extraction have included a scraper API in their offers. However, it’s important to do your research before you sign up for anything.
For example, some companies have adapted their APIs to work for specific use cases, such as E-Commerce Scraper API for the e-commerce sector, SERP Scraper API for SEO-related purposes, or Web Scraper API for large-scale website scraping (get more info).
Indeed, a good provider should have their products well-thought-out and suited for your business needs.
Final Thoughts
In summary, web scraping at scale can bring your company numerous benefits. However, before diving head first, you need to consider how you’re going to approach web scraping workflow.
Most importantly, make sure to use reliable tools that are able to effectively overcome common scraping challenges and get the results you need.
