When your scraping setup starts feeling like a toxic relationship... 😂💔 A dramatic breakup, a few hard truths, and the upgrade every developer deserves.
Scrapingdog
Software Development
Jaipur, Rajasthan 2,617 followers
Web scraping API for scalable data extraction
About us
Scrapingdog is a web scraping API that helps developers and data teams extract data from search engines, e-commerce platforms, and websites without managing proxies, browsers, or CAPTCHAs. The API returns clean HTML or structured data in a single request, handling IP rotation, browser rendering, retries, and anti-bot challenges automatically. It’s commonly used for SEO, SERP tracking, price monitoring, market research, collecting datasets for LLM training, and large-scale data collection. Scrapingdog is designed for teams that want reliable scraping infrastructure without building and maintaining their own proxy networks or browser stacks. The platform focuses on predictable performance, simple integration, and scalability across use cases. 📩 For support or partnerships: info@scrapingdog.com
- Website
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https://www.scrapingdog.com
External link for Scrapingdog
- Industry
- Software Development
- Company size
- 2-10 employees
- Headquarters
- Jaipur, Rajasthan
- Type
- Self-Owned
- Founded
- 2020
- Specialties
- Web Scraping API, Search Engine Scraping, E-commerce Data Extraction, Automated Data Extraction, Large-Scale Web Data Collection, SERP Tracking, and Anti-Bot & CAPTCHA Handling
Products
Locations
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Primary
Get directions
Jhalana Dungri Road
III florr , BTH
Jaipur, Rajasthan 302004, IN
Employees at Scrapingdog
Updates
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Access flight data with ease using the Google Flights API! Search and retrieve one-way, round-trip, and multi-city flight results directly from Google Flights, complete with powerful filtering and sorting options to fit your needs. Endpoint: https://lnkd.in/gbRrKaZZ Cost: 5 API credits per request Perfect for travel apps, fare tracking, flight comparison platforms, and travel analytics.
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The web scraping landscape has changed dramatically but not in the ways many expected. Whether you're a developer, data engineer, founder, or researcher, understanding where web scraping is heading is essential for building sustainable data pipelines. I'd love to hear your perspective: What do you think has been the biggest shift in web scraping over the last few years?
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Amazon Scraper API Update | Postal Code Fix & AU/UK Stability Restored This week's Scrapingdog update is here! Fixed Amazon Scraper API issues with the postal_code parameter Restored scraping stability for Amazon Australia 🇦🇺 Restored scraping stability for Amazon United Kingdom 🇬🇧 Your Amazon scraping workflows are now more reliable across supported marketplaces.
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A federal court just handed down a ruling that matters for anyone who works with public web data. A major search engine sued a data scraping company, claiming it had the authority to block scraping on behalf of copyright owners. The court dismissed the case because that authority was never proven. The takeaway is simple: putting a barrier around data doesn't create ownership. Without real, demonstrated authorization from rights holders, anti-scraping claims don't automatically hold up in court. This is a meaningful moment for developers, researchers, and businesses that rely on publicly accessible data to build products, conduct analysis, and drive innovation. The open web should stay open and rulings like this help keep it that way.
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Scrape Google Shopping data without the scraping headaches. With Scrapingdog's Google Shopping Scraper API, get fast, reliable Google Shopping results, no proxy rotation, no parsing hassles. Just clean, structured data at 10 credits per successful request. 🌐 https://lnkd.in/gH_UBc8Y
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Scrapingdog reposted this
I built a workflow that finds people actively complaining about my client's competitor. Every month. Automatically. (download link below) It's as easy as scraping TrustRadius reviews. Here's the workflow I use to turn them into pipeline: → Plug in any competitor URL, find their TrustRadius product page → Scrape all review pages with Scrapingdog (markdown, pagination is just page 2, 3, 4...) → Parse every review card: name, title, company, rating, review text → Skip "verified user", keep everyone with a real name → Filter for 1-3 star reviews, or reviews mentioning the exact weakness you solve → Resolve each person to their LinkedIn profile using Google AI Mode (also scraped via Scrapingdog, ~10 credits per call) → Extract the profile URL with a cheap model → Push everything into RevenueBase for emails and phone numbers Then the outreach writes itself: "Saw you mentioned [specific con] about [competitor]. That's the exact thing we built around." [Google AI Mode is absurdly good at "find this person's LinkedIn profile" — and scraping it costs almost nothing] [These people need zero education. They already bought the category. You only have to convince them your version is better.] Run it monthly. New reviews keep coming in. The list refills itself. Here's a link to the skill you can drop into Claude/Cursor to start running this today without building anything: https://lnkd.in/e3RcVHPY