Data Collection

Zillow hosts millions of real estate listings across the U.S., but manually collecting that data is slow and error-prone. This guide walks you through how to scrape Zillow data effectively and ethically. You’ll learn what kind of data is accessible, which tools to use, and how to handle anti-scraping challenges to keep your pipeline running smoothly.

Your company's data is everywhere – databases, spreadsheets, legacy systems, but most sits unused because nobody knows how to connect the pieces. Businesses waste millions rebuilding what already exists. Data mapping fixes this chaos. It bridges isolated information and creates actionable insights. AI now makes it automatic, and today we're exploring how it does so.

ZoomInfo is a goldmine for B2B teams – over 100M company profiles and 260M contacts, all in one place. But getting that data isn’t easy. With strict defenses like CAPTCHAs, browser fingerprinting, and aggressive IP bans, most scrapers fail after just a few requests. That’s where this guide comes in. We’ll show you how to bypass ZoomInfo’s countermeasures and extract clean, actionable data at scale.

After years of progress, AI has gotten a lot better at acting like human thinking. Whether that’s in machine learning, robotics, natural language processing (NLP), or training AI agents. But one thing still holds true – AI is only as good as the data it learns from. In this post, we’ll look at why high-quality training data matters so much when building strong AI systems.

Google Shopping is a product search engine that aggregates listings from thousands of online retailers. Businesses scrape it to track competitor pricing, spot trends, and gather valuable eCommerce insights. Using APIs, no-code tools, or custom scripts, you can extract data like product titles, prices, ratings, and more. In this guide, we’ll build a custom scraping script using Python and Playwright!

Google Scholar is a free search engine for academic articles, books, and research papers. If you're gathering academic data for research, analysis, or application development, this blog post will give you a reliable foundation. In this guide, you'll learn how to scrape Google Scholar with Python, set up proxies to avoid IP bans, build a working scraper, and explore advanced tips for scaling your data collection.

Scraping Amazon without proxies leads to IP bans, CAPTCHAs, and rate limits, making data collection nearly impossible. Proxies are essential for bypassing these defenses and accessing vital pricing and product data. This guide explains why scraping Amazon is challenging, how proxies can help, and which types of proxies are most effective for reliable, large-scale Amazon data extraction.

Web scraping should be simple. Yet, choosing the right solution often feels like a challenge – different APIs for different targets, multiple subscriptions, and unnecessary complexity. That’s why we’re introducing a more convenient way to collect data from various targets – our four scraping APIs are becoming one, more powerful than ever, Web Scraping API. Now, you can collect data from all targets – eCommerce, SERPs, social media, and web, with one unified API.

If you’re here, you already know Amazon constantly tweaks product prices. The eCommerce giant makes around 2.5 million price changes daily, resulting in the average item seeing new pricing roughly every ten minutes. For sellers, marketers, and savvy shoppers, that creates both a challenge and an opportunity.

This comprehensive guide walks you through proven methods – from Excel's built-in tools to powerful scraping APIs that can simplify your Amazon price monitoring workflow.

As a data scientist, you’re already using R for data analysis and visualization. But what if you could also conveniently use it to gather data directly from websites? With the R programming language, you can seamlessly scrape static pages, HTML tables, and even dynamic content. Let’s explore how you can take your data collection to the next level!

This comprehensive guide will teach you how to scrape Amazon product data using Python. Whether you’re an eCommerce professional, researcher, or developer, you’ll learn to create a solution to extract valuable insights from Amazon’s marketplace. By following this guide, you’ll acquire practical knowledge on setting up your scraping environment, overcoming common challenges, and efficiently collecting the needed data.

Web scraping with Python is a powerful technique for extracting valuable data from the web, enabling automation, analysis, and integration across various domains. Using libraries like Beautiful Soup and Requests, developers can efficiently parse HTML and XML documents, transforming unstructured web data into structured formats for further use. This guide explores essential tools and techniques to navigate the vast web and extract meaningful insights effortlessly.

Proxy errors like 404, 407, or 503 can be frustrating roadblocks – but they’re also valuable clues. These HTTP status codes point to specific issues in the communication between your browser, proxy, and the target server. Whether it's a client-side misconfiguration or a server-side block, understanding these errors is the first step to resolving them. In this guide, we’ll break down the most common proxy errors and walk you through practical solutions to fix them fast.

They say you’ll never have time to read all the books or watch all the movies in your entire lifetime – but what if you could at least gather all their titles, ratings, and reviews in seconds? That’s the magic of web scraping: automating the impossible, collecting large amounts of data, and uncovering hidden insights from all across the internet. In this article, we’ll explore valuable web scraping ideas that you can create even with little to no experience – completely free of charge.

Amazon’s massive product ecosystem makes it a goldmine for price tracking, competitive analysis, and market research. This guide covers methods for Amazon price scraping, from small-scale tracking to enterprise-grade solutions, plus how to import data into Google Sheets for real-time analysis. Whether you’re hunting deals or analyzing eCommerce trends, we’ve got you covered.

Keeping up with everything happening around the world can feel overwhelming. With countless news sites competing for your attention using catchy headlines, it’s hard to find what you need among celebrity tea and what the Kardashians were up to this week. Fortunately, there’s a handy tool called Google News that makes it easier to stay informed by helping you filter out the noise and focus on essential information. Let’s explore how you can use Google News together with Python to get the key updates delivered right to you.

Artificial Intelligence powers everything from chatbots to complex data analysis tools. But behind the sleek interfaces and impressive capabilities lies a hidden process – the petabytes of data collected. We sat down with our CEO, Vytautas Savickas, to discuss the AI revolution and how data is being collected to fuel various tools.

Because the internet never forgets and every click leaves a digital footprint, hiding your IP address is sometimes essential. Staying anonymous enhances data security and grants access to geo-restricted content, though it comes with a few trade-offs. This guide covers 5 effective ways to hide your IP and protect your online privacy.