How to successfully build an Enterprise Data Extraction infrastructure

Building an enterprise data extraction infrastructure can be a daunting task, but it doesn't have to. First, your business must have a clear understanding on how to build an enterprise data extraction infrastructure that scales efficiently.

Web scraping projects are made up of different elements and it is key to find the right procedure that suits your custom needs in a sustainable way. Many organizations seem to struggle to find developers with the right expertise, budgets are hard to forecast, or they just can’t find solutions that fit their needs.

To help you get a better understanding of the process, we've put together an outline of the key steps involved in building an effective infrastructure. 

Whether you're looking to extract data for lead generation, price intelligence, market research and so on, this article will help you. You will understand the importance of a scalable architecture, high performing configurations, crawl efficiency, proxy infrastructure and automated data QA.

In order to get the most valuable data possible, it’s important that your web scraping project elements have a well-crafted and scalable architecture.

Strategic decision-making with a scalable architecture 

The first building block of any large scale web scraping project is to develop a scalable architecture. It's important to have a well-crafted index page that contains links to all the other pages that need extracting. Index pages can be difficult to build, but with the help of an enterprise data extraction tool, it can be done quickly and easily.

In most cases, there will be some form of index page that contains links to numerous other pages that need to be scraped. In the case of e-commerce, these pages are typically category “shelf” pages that contain links to numerous product pages.

For blog articles, there is always a blog feed that contains links to each of the individual blog posts. However, to scale enterprise data extraction you really need to separate your discovery spiders from your extraction spiders.

When the case refers to enterprise e-commerce data extraction, this would be developing one spider, the product discovery spider, to discover and store the URLs of products in the target category, and another spider to scrape the target data from the product pages.

Using this approach allows you to split the two core processes of the web scraping, crawling and scraping, and enables the allocation of more resources to one process over the other, and helps you avoid bottlenecks. 

High performing hardware configurations

The most important consideration for building a high output enterprise data extraction infrastructure is spider design and crawling efficiency. After developing a scalable architecture during the planning stages of your data extraction project, the next fundamental foundation you need to develop when scraping at scale is configuring your hardware and spiders for high performance.

Oftentimes, when developing enterprise data extraction projects at scale, speed is the most important concern. In a lot of applications, enterprise scale spiders need to have finished their full scrape in a defined period of time. E-commerce companies use price intelligence data to adjust their pricing, their spiders need to have scraped their competitors' entire catalogue of products within a couple hours so that they can adjust.

Key steps teams must consider for the configuration process: 

1. Develop a deep understanding of the web scraping software
2. Finetune your hardware and spiders to maximize crawling speed
3. Make sure you have the right hardware and crawling efficiency with which to scrape at scale
4. Ensure you're not wasting team efforts on unnecessary processes
5. Remember speed is a high priority to consider while deploying configurations

This need for speed poses big challenges when developing an enterprise level web scraping infrastructure. Your web scraping team will need to find ways to squeeze every last ounce of speed out of your hardware and make sure that it isn’t wasting fractions of a second on unnecessary processes.

To do this, enterprise web scraping teams need to develop a deep understanding of the web scraper software market and frameworks they are using.

Quick and reliable crawl efficiency

You always need to be focused on crawling efficiency and robustness, in order to scale your enterprise data extraction project. Your goal should always be to solely extract the exact data you need in as few requests and as reliably as possible.

Any additional requests or data extraction slow the pace at which you can crawl a website.

Not only do you have to navigate potentially hundreds of websites with sloppy code, you will also have to deal with constantly evolving websites. 

A good rule of thumb is to expect your target website to make changes that will break your spider (drop in data extraction coverage or quality) every 2-3 months.

Instead of having multiple spiders for all the possible layouts a target website might use, it is best practice to have only one product extraction spider that can deal with all the possible rules and schemes used by different page layouts. The more configurable your spiders are the better. 

As mentioned above, only use a headless browser, such as Splash or Puppeteer to deploy serverless functions, and render javascript as a last resort. Rendering javascript with a headless browser while crawling is very resource intensive and severely impacts the speeds at which you can crawl. Don’t request or extract images unless you really have to. 

If you can get the data you need from a single page without requesting each individual item page then always confine your scraping to the index/category page. 

An example of this is scraping product data, if you can get the data you need from the shelf page (e.x. Product names, price, ratings, etc.) without requesting each individual product page, then don’t move forward with the additional request.

Most companies need to extract product data on a daily basis, which means waiting a couple days for your engineering team to fix any broken spiders and that isn’t always a viable option. 

When these situations arise, Zyte uses a machine learning based data extraction tool that we’ve developed as a fallback until the spider has been repaired. This ML-based extraction tool automatically identifies the target fields on the target website (product name, price, currency, image, SKU, etc.) and returns the desired result. 

A robust proxy infrastructure that targets specific data

You also need to build a scalable proxy management infrastructure for your enterprise data extraction project. Having a robust proxy management system is critical if you want to be able to reliably scrape the web at scale and target location specific data.

Without healthy and well-managed proxies, your team will quickly find itself spending most of its time trying to manage proxies and will be unable to adequately scrape at scale.

If you want your enterprise data extraction to deliver at scale, you will need a large list of proxies and will need to implement the necessary IP rotation, request throttling, session management and blacklisting logic to prevent your proxies from getting blocked.

To ensure you can achieve the necessary daily throughput from your spiders you’ll often need to design your spider to counteract anti-bot countermeasures without using a headless browser such as Splash or Puppeteer.

These browsers render any Javascript on the page but as they are very heavy on resources they drastically slow the speed at which you can scrape a website. Making them practically unusable when scraping at scale, other than an edge case where you have pursued every other available option.

Automated data QA system that scales

The key component of any enterprise data extraction project is having a system for automated data quality assurance. Data quality assurance is often one of the most overlooked aspects of web scraping. Everyone is so focused on building spiders and managing proxies that they rarely think about QA until they are running into serious problems.

At its core, an enterprise data extraction project is only as good as the data it can produce. Even if you have the fanciest web scraping infrastructure on the planet, unless you have a robust system to ensure you are getting a reliable stream of highly qualified information for your enterprise data extraction project.

The key to data quality assurance for large scale web scraping projects is making it as automated as possible. If you are scraping millions of records per day, it is impossible to manually validate the quality of your data.

Conclusion

The best way to ensure that you build a successful enterprise data extraction infrastructure is by understanding where your data requirements lie and then designing your architecture accordingly to cater to those needs. It’s also important not to ignore crawl efficiency while developing such an architecture.

Once all of the different elements for enterprise data extraction to succeed (with high-quality data extraction automation) are ready and running smoothly, analyzing reliable and valuable data will be an easy process. Providing with the peace of mind of knowing that your organization has nothing to worry about when tackling such projects. 

Now that you've seen the best tips and procedures that assure quality data with enterprise web scraping, it's time to get started on building your own enterprise web scraping infrastructure. 

Get in touch with our team of expert developers to see how easy it can be to manage these processes.