What Lessons We Have Learnt For Data Price Intelligence After Extracting 100 Billion Product Pages?

Web scraping could look extremely easy these days. You can have many open-source frameworks or libraries, data scraping tools, and visual extracting tools, which make that extremely easy to extract data from websites. Although, when you wish to extract a website at scale, things will start getting very fast and tricky, particularly when it comes to data price intelligence, where quality and scale are very important.

In our series of blogs here, we will reveal with you different lessons that we’ve learned after scraping more than 100 billion product pages since our inception to provide you in-depth looks at challenges you would face while scraping products data from e-commerce stores and share the finest practices for addressing those challenges.

For people, who are fascinated about data scraping at scale, however, are struggling with the choice of whether or not you, need to create an in-house web scraping team or outsource that to any enthusiastic web scraping company can contact X-Byte Enterprise Crawling before taking any decisions.

Unlike any normal web scraping application, extracting e-commerce product data has a distinctive group of challenges, which make data scraping much more difficult.

Primarily, these challenges could be nailed down to a couple of things: data quality and speed.

As time is generally a regulating constraint, extraction at scale needs crawlers to extract the web at high speeds without quality compromise of data. This requirement for speed makes extracting larger volumes of data extremely challenging.

This could be obvious and it could not be the best of the challenges, however, chaotic and ever-changing website formats are the biggest challenge that you would face while scraping data at scale. Not inevitably due to the task complexity, but the timing and sources you would spend to deal with that.

In case, you spent any length of time creating scrapers for the e-commerce stores then you’ll understand that there are wide-ranging sloppy codes on the e-commerce stores. It has more to it than the HTML’s well-formedness or occasional characters encoding the problem. We’ve gone through all kinds of colorful problems over the years including distorted HTTP response codes, misused Ajax, or broken Java Scripts.

While scraping at scale, you need to navigate possibly hundreds of sites having sloppy codex, you would also need to cope with continuously evolving sites. A good option is to anticipate your targeted website to do changes, which would break the spider (drop-in web extraction quality or coverage) every 2 to 3 months.

Different variations in the site layouts from multilingual and regional sites, A/B split testing, as well as packaging or pricing variants, create a complete world of problems, which routinely break the spiders.

The following challenge you would face is creating a crawling infrastructure, which would scale total requests every day without degrading the performance.

While scraping products data at scale, an easy web crawler, which crawls as well as scrapes data successively just doesn’t cut that. Usually, a serial data scraper will do requests in the loop with every request taking 2 to 3 seconds for completion.

The approach looks good in case your scraper is only needed to make more than 40,000 requests every day (requests every 2 seconds equivalents 43,200 requests every day). Although, you will have to change to the crawling architecture, which will permit you to extract millions of requests every day without any decrease in performance.

Extracting at scale could easily get compared with Formula 1 whereas your objective is removing every needless weight from the car and crush that last part of horsepower from your engine with speed. A similar thing is true with web scraping at scale.

While scraping a large data volume, you will always look for ways of minimizing the requests cycle time as well as maximize the spider’s performance of accessible hardware sources. You just need to hope that you could save a couple of milliseconds from every request.

To do that, your team needs to create a deep understanding of the web scraping framework, hardware, and proxy management you are using so that you can adjust them for the best performance.

In case, you are extracting e-commerce sites at scale, you are assured to run the websites employing all anti-bot countermeasures.

For small websites, the anti-bot countermeasures would be straightforward (ban IPs creating excessive requests). Although, bigger e-commerce sites like Amazon, etc. use refined anti-bot countermeasures like Incapsula, Akamai, or Distil Networks that make scraping data considerably more difficult.

Keeping this in mind, the initial and the most important requirement for scraping products data at scale is using proxy IPs. While extracting at a scale, you would require a large list of proxies as well as would require to implement required IP rotations, session management, and request throttling, as well as blacklisting the logic for preventing proxies from being blocked.

From the perspective of a data scientist, the most significant consideration for any data scraping project is data quality. Scraping at scale merely makes that the data quality is more important.

While scraping millions of data every day, it is very impossible to physically verify that the data is intact and clean. It is extremely easy for incomplete or dirty data to sneak into the data feeds as well as disrupt all your efforts made on data analysis.

It is particularly true when extracting products on different versions of a similar store (various regions, languages, etc.) or different stores.

Outside the careful QA procedure during the design phase of creating a spider, when the codes of a spider are tested and peer-reviewed to make sure that it is scraping the required data in the most dependable way possible. The finest method of making sure the highest quality data possible is the development of the auto QA monitoring system.

As you can see, extracting product data at scale makes its own distinctive set of experiments. Hopefully, this blog has given you more knowledge about the challenges you would face as well as how you can solve them.

At X-Byte Enterprise Crawling, we focus on turning unstructured data into well-structured data. In case, you like to know more about how you may use web extracted product data for your business then you can contact us anytime and our team will walk you through services that we offer from startups to Fortune 100 companies!