Driven by an ever-increasing demand to capture information, large scale eCommerce providers are recognizing the value which can be seen from extracting publicly available information.
Finding a more manageable solution for a large-scale data gathering has been on the minds of many in the web scraping community. Specialists have seen a lot of potential in applying AI (Artificial Intelligence) and ML (Machine Learning) to web scraping. However, actions toward data gathering automation that uses AI applications are only just coming to light. This is no surprise, 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, organizations can 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 the challenges.
The web scraping process
Firstly, web scraping is made up of 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 shows 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 project. Let’s examine these in a little more detail:
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.
These are just the tip of the iceberg organizations 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 data gathering. 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 a business is relying on a proxy vendor, 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 the developer teams attention more often than expected.
Traditional web scraping comes with many challenges, requires a lot of manual labor, time, and resources. However, the bright side with computing is almost all things can be automated. As the development of AI and ML powered web scraping emerges, future-proof large-scale data gathering will become a more realistic solution.
Making web scraping future-proof
The power of AI and ML is able to allow web scraping to improve on a monumental scale. 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 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.
We’re only at the beginning
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.
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. 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.
Julius Cerniauskas, CEO, Oxylabs