How to collect web data at scale and overcome obstacles
Contents of article:
- What is web scraping at scale?
- What are the obstacles to web scraping at scale?
- How to build highly-functional scrapers, relying on proxies, to overcome obstacles?
Data volumes expand exponentially. The world’s accumulated amount of data generated hit 64 zettabytes back in 2020, including information captured, copied, consumed, etc. . Soon, around 2025, it will exceed 181 zettabytes. Businesses need those never-ending heterogeneous datasets. It means companies will continue investing in web scraping, dedicating effort, time, funds. This piece describes what such data collection at scale presupposes, related obstacles, and ways to overcome them.
Working as a website with geo targeted proxies, Dexodata knows much about web scraping at scale. Ambitious data harvesting initiatives serve as reasons why customers contact us. This substantial track record gives us sufficient expertise covering data collection upscaling challenges.
Initially, web scraping looks like no big deal. People routinely activate open-source libraries, frameworks, ready-to-use scraping tools, proxy ecosystems, making data collection easier (Python is acknowledged as the doubtless leader). Nonetheless, as data scraping intensifies, it becomes more challenging.
What is web scraping at scale?
Web scraping, as such, entails extracting data from websites automatically, typically with rotating proxies. In case professionals mention “web scraping at scale”, two scenarios are possible:
- Issuing numerous concurrent requests towards a single website for acquiring as much data as feasible, within limited timeframes.
- Sending parallel queries towards multiple sources simultaneously.
In both schemes, at-scale approaches revolve around the systematic accumulation of extensive datasets.
What are the obstacles to web scraping at scale?
Issues making web scraping at scale tricky include:
- IP restrictions. Though scraping public data is ethical, sites do not like bots. Whenever a suspicious IP gets detected or there are too many queries stemming from the same location, platforms impose restrictions. Use proxies, ideally rotating residential proxies or mobile network proxies, for bypassing such obstacles.
- CAPTCHA prompts are capable of hindering data harvesting at scale. Solving CAPTCHAs poses challenges for scraping scripts. Yet there exist services, e.g. Anti-Captcha or 2Captcha, providing automated CAPTCHA-solving options (fees apply). Integrate anti-CAPTCHAs with web scraping scripts, building on proxies.
- Site layout changes Another data harvesting at scale impediment Dexodata draws attention towards is . Web scraping is closely tied to the UI-questions, its structures. If target sites undergo alterations, web scrapers could “crack” or collect inaccurate, irrelevant information. This is a frequent occurrence, making the ongoing maintenance of scrapers with proxies more resource-intensive and time-consuming than their initial development. To tackle this situation, establish test cases for data retrieval logic and run them on a daily or weekly basis, either through manual execution or automated tools. Make sure dynamic proxies with adequate schedules are in place. This enables one to monitor whether the pages have undergone any modifications.
- Server crashes and rate limits. Websites can experience query overload during peak hours. If users add the web scraping at scale factor, servers might crash outright. That is counterproductive, unethical, harmful. So schedule scripts, so that they avoid congestion. On top of that, platforms may introduce rate curtailments to control amounts of requests a client can make within a defined period. Keep web scraping sessions not too intensive.
- Dynamic content. Web scraping at scale becomes complicated when handling websites that, say, utilize JS to display content dynamically. It is not uncommon for libraries or frameworks to only access and retrieve the information present in primary HTML documents.
At the same moment, one could name alternatives potent enough in terms of overcoming dynamic content issues. Our website with proxies reminds of Selenium, for instance.
How to build highly-functional scrapers, relying on proxies, to overcome obstacles?
While using ready-made web scraping solutions seems logical and economical, optimized courses of action imply tailored-fit scraping software, focused on sites of particular importance. Follow this checklist from Dexodata, a proxy ecosystem for data harvesting at scale when crafting it:
| Outline sites | Tailor scrapers to clearly outlined databases, one-of-a-kind or several ones, united by common properties |
| Set connection queries | Delineate the techniques of sending HTTP queries. They vary pertaining to web scraping methods |
| HTML parsing | Locate desired elements |
| Data extraction | These might be incremental streams, batches, full data extraction |
| Data cleaning | Set formatting standards to make data accurate, consistent, ready for assessment |
| Data saving | Define how future datasets will be saved and stored. |
From our side, Dexodata promises ethically sourced IPs, obtained through the informed consent principles. Our KYC/AML policies concerning proxies are detail-oriented, rigorous, all-encompassing. Information collection at scale is doable from 100+ countries, comprising USA, EU, Turkey, Russia, South America, Kazakhstan, and more.
Construct unique combos for distinctive at scale projects. eCommerce shops, social media, news outlets, blogs, travel fare aggregators, shared data-sheets, market research reports, price tables, reviews are all within reach.
Proxies’ free trial is offered when newcomers register.
