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Ensuring web data quality at scale means moving beyond fragile scripts and spot checks to robust validation that keeps business decisions accurate and reliable.

The proxy era is ending as web scraping shifts from managing IP pools to smarter, API-driven solutions.

There’s a popular archetype in web scraping circles: the heroic engineer who fights CAPTCHAs at 3 a.m., hand-tunes a proxy farm before breakfast, then rewrites four spiders after lunch because the target sites pushed new JavaScript.

The practice of data quality (DQ) is emerging as a key discipline businesses can use to understand and improve the provenance of the content they collect.

Despite their benefits, web scraping APIs are sometimes misunderstood. So, let’s debunk some of the most common myths.

The emergence of hybrid DaaS/SaaS models is a logical response to the complex realities of modern data needs.

Discover how AI and LLMs are enhancing web scraping with smarter crawling, fuzzy data extraction, automated spider generation, and intelligent QA.

Scaling your business’ web data gathering – acquiring, monitoring and storing a growing amount of data from a growing number of sources over time – requires holistic planning.

Engagements with data suppliers break down when buyers don’t have a clear project concept. Understanding and articulating your needs is paramount. Meet the three types of data buyers. Which one are you?

The story of Scrapy reflects the broader evolution of the web itself and the ongoing quest to harness its ever-expanding ocean of information.

Unlock the future of web scraping with natural language—making data extraction faster, easier, and accessible to all.

This article reveals the approach that makes once-unviable web data projects possible.