Due to the benefits that data-based decision-making may bring to maintaining competitiveness in an industry with such thin margins, web scraping is already pervasive among large e-commerce enterprises.

Online retailers are increasingly employing site data to support their research into competitors, dynamic pricing, and new product development.

These e-commerce sites’ top priorities are their data feed’s dependability and capacity to deliver the data they require at the required frequency.

So that they can reliably scrape the web without interruption, many e-commerce companies encounter significant difficulties in managing their proxies.

We’ll discuss these difficulties in this post, along with strategies used by the top online scrapers to overcome them.

Challenge #1: The massive amount of demands

A major difficulty for businesses is the sheer volume of requests being made (upwards of 10 million successful requests each day). Companies need thousands of IPs in their proxy pools to handle the daily millions of requests that come in.

To be able to scrape the precise data they require properly, they need not only a huge pool size but also a pool that has a variety of proxy kinds (location, data center/residential, etc.).

However, running such a large number of proxy pools might take a lot of time. Developers and data scientists frequently claim that they spend more time managing proxies and resolving data quality problems than they do actually examining the extracted data.

You must add a strong intelligence layer to your proxy management logic in order to handle this degree of complexity and scrape the web at this scale.

Managing your proxy pool will be more effective and hassle-free the more advanced and automated your proxy management layer is.

Let’s continue on that note by learning more about proxy management layers and how the top e-commerce businesses overcome their problems.

Challenge #2 – Building a solid intelligence layer 

If your spiders are well-designed and you have a sizable pool, you can get away with a simple proxy management infrastructure when scraping the web on a small scale (a few thousand pages per day).

However, this won’t cut it when you are scraping the web on a large scale. When developing a large-scale web scraper, you’ll immediately encounter the following difficulties.

• Ban identification – Your proxy solution must recognize various ban kinds to diagnose and resolve the underlying issue, such as captchas, redirects, blocks, ghosting, etc. The fact that your solution must additionally develop and maintain a ban database for each and every website you scrape makes things more challenging.

• Retry Errors – Your proxies must be able to retry the request with different proxies if they encounter any errors, bans, timeouts, etc.

• Request Headers- A healthy crawl depends on managing and rotating user agents, cookies, etc.

• Control Proxies – You may need to maintain a session with the same proxy for some scraping jobs. Therefore, you’ll need to configure your proxy pool to support this.

• Add Delays – Randomize delays and request throttling automatically to assist in masking the fact that you are scraping and accessing challenging websites.

• Geographical Targeting – You may occasionally need to set up your proxy pool such that only a subset of proxies is used on a given website.

Businesses need to implement a strong proxy management logic to manage sessions, user agents, blacklisting logic, throttle requests, identify bans and captchas, identify bans and captchas, identify bans and captchas, identify bans and captchas, identify bans and captchas, and automate retries in order to prevent their proxies from being blocked and disrupting their data feed.

Challenge #3 – Accuracy and Availability of data

E-commerce product data often varies by user location, including prices and specs.

Companies often request each product from several locations/zip codes to get the most accurate pricing or feature data. This makes an e-commerce web scraping proxy pool more complicated because it needs proxies from multiple locations and logic to select the right ones for the target areas.

At lesser volumes, manually configuring a proxy pool to use certain proxies for web scraping projects works well. As web scraping initiatives grow, this can get complicated. Scaled scraping requires automated proxy selection.

The issue is that most available solutions sell simply proxies or, at most, proxies with basic rotation logic. Companies must frequently create and improve this sophisticated proxy management layer themselves. This calls for substantial development.

Frequently asked questions:

Can I use an API gateway as a proxy?

You can access your backend services using either an API proxy or an API gateway. Even a basic API proxy can function as an API gateway.

What does scraping proxies accomplish?

Web scraping uses proxies to get around scraper blocking or to access content that is geo-restricted.

Can you explain what a proxy API is?

A software component known as an API proxy connects to back-end services and then generates a more useful and current API to connect to the front end. With the aid of API proxies, developers can define an API without having to modify the underlying back-end services.