The New Beginnings of AI-Powered Web Data Gathering Solutions
Are you approaching data gathering on a large scale in a traditional manner? If so, expect to invest a lot of time and effort into proxy infrastructure maintenance.
Data gathering consists of many time-consuming and complex activities. These include proxy management, data parsing, infrastructure management, overcoming fingerprinting anti-measures, rendering JavaScript-heavy websites at scale, and much more. Is there a way to automate these processes? Absolutely.
Finding a more manageable solution for a large-scale data gathering has been on the minds of many in the web scraping community. Specialists saw a lot of potential in applying AI (Artificial Intelligence) and ML (Machine Learning) to web scraping. However, only recently, actions toward data gathering automation using AI applications have been taken. This is no wonder, as AI and ML algorithms became more robust at large-scale only in recent years together with advancement in computing solutions.
By applying AI-powered solutions in data gathering, we can help automate tedious manual work and ensure a much better quality of the collected data. To better grasp the struggles of web scraping, let’s look into the process of data gathering, its biggest challenges, and possible future solutions that might ease and potentially solve mentioned challenges.
Data collection: step by step
To better understand the web scraping process, it’s best to visualize it in a value chain:
As you can see, web scraping takes up four distinct actions:
- Crawling path building and URL collection.
- Scraper development and its support.
- Proxy acquisition and management.
- Data fetching and parsing.
Anything that goes beyond those terms is considered to be data engineering or part of data analysis.
By pinpointing which actions belong to the web scraping category, it becomes easier to find the most common data gathering challenges. It also allows us to see which parts can be automated and improved with the help of AI and ML powered solutions.
Large-scale scraping challenges
Traditional data gathering from the web requires a lot of governance and quality assurance. Of course, the difficulties that come with data gathering increase together with the scale of the scraping project. Let’s dig a little deeper into the said challenges by going through our value chain’s actions and analyzing potential issues.
Building a crawling path and collecting URLs
Building a crawling path is the first and essential part of data gathering. To put it simply, a crawling path is a library of URLs from which data will be extracted. The biggest challenge here is not the collection of the website URLs that you want to scrape, but obtaining all the necessary URLs of the initial targets. That could mean dozens, if not hundreds of URLs that will need to be scraped, parsed, and identified as important URLs for your case.
Scraper development and its maintenance
Building a scraper comes with a whole new set of issues. There are a lot of factors to look out for when doing so:
- Choosing the language, APIs, frameworks, etc.
- Testing out what you’ve built.
- Infrastructure management and maintenance.
- Overcoming fingerprinting anti-measures.
- Rendering JavaScript-heavy websites at scale.
These are just the tip of the iceberg that you will encounter when building a web scraper. There are plenty more smaller and time consuming things that will accumulate into larger issues.
Proxy acquisition and management
Proxy management will be a challenge, especially to those new to scraping. There are so many little mistakes one can make to block batches of proxies until successfully scraping a site. Proxy rotation is a good practice, but it doesn’t illuminate all the issues and requires constant management and upkeep of the infrastructure. So if you are relying on a proxy vendor, a good and frequent communication will be necessary.
Data fetching and parsing
Data parsing is the process of making the acquired data understandable and usable. While creating a parser might sound easy, its further maintenance will cause big problems. Adapting to different page formats and website changes will be a constant struggle and will require your developers teams’ attention more often than you can expect.
As you can see, traditional web scraping comes with many challenges, requires a lot of manual labour, time, and resources. However, the brightside with computing is that almost all things can be automated. And as the development of AI and ML powered web scraping is emerging, creating a future-proof large-scale data gathering becomes a more realistic solution.
Making web scraping future-proof
In what way AI and ML can innovate and improve web scraping? According to Oxylabs Next-Gen Residential Proxy AI & ML advisory board member Jonas Kubilius, an AI researcher, Marie Sklodowska-Curie Alumnus, and Co-Founder of Three Thirds:
“There are recurring patterns in web content that are typically scraped, such as how prices are encoded and displayed, so in principle, ML should be able to learn to spot these patterns and extract the relevant information. The research challenge here is to learn models that generalize well across various websites or that can learn from a few human-provided examples. The engineering challenge is to scale up these solutions to realistic web scraping loads and pipelines.”
Instead of manually developing and managing the scrapers code for each new website and URL, creating an AI and ML-powered solution will simplify the data gathering pipeline. This will take care of proxy pool management, data parsing maintenance, and other tedious work.
Not only does AI and ML-powered solutions enable developers to build highly scalable data extraction tools, but it also enables data science teams to prototype rapidly. It also stands as a backup to your existing custom-built code if it was ever to break.
What the future holds for web scraping
As we already established, creating fast data processing pipelines along with cutting edge ML techniques can offer an unparalleled competitive advantage in the web scraping community. And looking at today’s market, the implementation of AI and ML in data gathering has already started.
For this reason, Oxylabs is introducing Next-Gen Residential Proxies which are powered by the latest AI applications.
Next-Gen Residential Proxies were built with heavy-duty data retrieval operations in mind. They enable web data extraction without delays or errors. The product is as customizable as a regular proxy, but at the same time, it guarantees a much higher success rate and requires less maintenance. Custom headers and IP stickiness are both supported, alongside reusable cookies and POST requests. Its main benefits are:
- 100% success rate
- AI-Powered Dynamic Fingerprinting (CAPTCHA, block, and website change handling)
- Machine Learning based HTML parsing
- Easy integration (like any other proxy)
- Auto-Retry system
- JavaScript rendering
- Patented proxy rotation system
Going back to our previous web scraping value chain, you can see which parts of web scraping can be automated and improved with AI and ML-powered Next-Gen Residential Proxies.
The Next-Gen Residential Proxy solution automates almost the whole scraping process, making it a truly strong competitor for future-proof web scraping.
This project will be continuously developed and improved by Oxylabs in-house ML engineering team and a board of advisors, Jonas Kubilius, Adi Andrei, Pujaa Rajan, and Ali Chaudhry, specializing in the fields of Artificial Intelligence and ML engineering.
Wrapping up
As the scale of web scraping projects increase, automating data gathering becomes a high priority for businesses that want to stay ahead of the competition. With the improvement of AI algorithms in recent years, along with the increase in compute power and the growth of the talent pool has made AI implementations possible in a number of industries, web scraping included.
Establishing AI and ML-powered data gathering techniques offers a great competitive advantage in the industry, as well as save copious amounts of time and resources. It is the new future of large-scale web scraping, and a good head start of the development of future-proof solutions.
