Data Collection

Real estate web scraping has become an essential way to collect up-to-date property data from platforms like Zillow, Realtor.com, Redfin, Rightmove, and Idealista without manual effort. Automated extraction helps individuals and businesses track prices, compare neighborhoods, and monitor supply trends with higher accuracy. In this guide, you'll get a practical overview of the tools, methods, and considerations involved in working with real estate listings as structured data for analysis, research, and everyday business use.

Web scraping is a great way to automate the extraction of data from websites, and curl is one of the simplest tools to get started with. This command-line utility lets you fetch web pages, send requests, and handle responses without writing complex code. It's lightweight, pre-installed on most systems, and perfect for quick scraping tasks. Let's dive into everything you need to know.

Web scraping is the process of automating page requests, parsing the HTML, and extracting structured data from public websites. While Python often gets all the attention, Java is a serious contender for professional web scraping because it's reliable, fast, and built for scale. Its mature ecosystem with libraries like Jsoup, Selenium, Playwright, and HttpClient gives you the control and performance you need for large-scale web scraping projects.

Nasdaq offers a wealth of stock prices, news, and market reports. Manually collecting this data is a Sisyphean task, since new information appears constantly. Savvy investors, analysts, and traders turn to web scraping instead, automating data gathering to power more intelligent analysis and trading strategies. This guide walks you through building a Nasdaq scraper with Python, browser automation, APIs, and proxies to extract both real-time and historical market data.

If you need a bunch of images and the thought of saving them one by one already feels tedious, you're not alone. This can be especially draining when you're preparing a dataset for a machine learning project. The good news is that web scraping makes the whole process faster and far more manageable by letting you collect large quantities of images in just a few steps. In this blog post, we'll walk you through a straightforward way to grab images from a static website. We'll use Python, a few handy libraries, and proxies to keep things running smoothly.

Web scraping Airbnb (a global platform for short-term rentals and experiences) involves automatically extracting data from listings to reveal insights unavailable through the platform itself. It's useful for analyzing markets, tracking competitors, or even planning personal trips. Yet, Airbnb's anti-scraping defenses and dynamic design make it a technically demanding task. This guide will teach you how to scrape Airbnb listings successfully using Python.

Scraping hotel listings is a powerful tool for gathering comprehensive data on accommodations, prices, and availability from various online sources. Whether you're looking to compare rates, analyze market trends, or create a personalized travel plan, scraping allows you to efficiently compile the information you need. In this article, we'll explain how to scrape hotel listings, ensuring you can leverage this data to its fullest potential.

HTML tables are one of the most common ways websites organize data – financial reports, product listings, sports scores, population statistics. But this data is locked in the webpage's layout. To use it, you need to extract it. This guide will show you how to do it using Python, starting with simple static tables and working up to complex dynamic ones.

Pagination is the system websites use to split large datasets across multiple pages for faster loading and better navigation. In web scraping, handling pagination is essential to capture complete datasets rather than just the first page of results. This guide explains what pagination is, the challenges it creates, and how to handle it efficiently with Python.

Craigslist is known as a valuable source of classified data across jobs, housing, and marketplace items for sale. However, scraping Craigslist presents challenges like CAPTCHAs, IP blocks, and anti-bot measures. This guide walks you through three Python scripts for extracting housing, job, and for sale item listings while handling these obstacles effectively with proxies or a scraper API.

While using Google Trends, you can discover the search interest rates for specific keywords during specific time frames in specific regions and analyze the popularity of Search keywords over time. This makes it possible to see how popular a topic is over time and across regions, without exposing sensitive search data. In this guide, we'll explain the kinds of data available from Google Trends, compare scraping techniques, and demonstrate two methods of gathering Google Trends data.

Production RAG fails when it relies on static knowledge that goes stale. This guide shows you how to build RAG systems that scrape live web data, integrate with LlamaIndex, and actually survive production. You'll learn to architect resilient scraping pipelines, optimize vector storage for millions of documents, and deploy systems that deliver real-time intelligence at scale.

In September 2025, Google officially discontinued the num=100 parameter. If you're an SEO professional, data analyst, or someone who prefers viewing all results at once, you've likely already felt the impact on your workflows. In this article, we'll explain what changed, why Google likely made this move, who it affects, and most importantly, how to adapt.

AI has evolved from programs that just follow rules to systems that can learn and make decisions. Businesses that understand this shift can leverage AI to tackle complex challenges, moving beyond simple task automation. In this guide, we'll walk you through how to connect modern AI tools with live web data to create an automated system that achieves a specific goal. This will give you a solid foundation for building even more sophisticated autonomous applications.

Web scraping extracts data from websites using automated tools. It's become a standard practice for businesses gathering competitive intelligence, training AI models, and building data-driven products. But the big question remains – is web scraping legal? The answer depends on what you scrape, how you scrape it, where the data comes from, and what you do with it next.

Yahoo Finance is one of the most comprehensive free financial data platforms available, offering real-time stock prices, historical data, and company fundamentals. However, scraping such a platform presents challenges like sophisticated anti-bot measures, JavaScript-heavy rendering, and dynamic content loading. This guide offers practical, tested methods for efficiently extracting Yahoo Finance data while navigating these obstacles.

GPT models power 92% of Fortune 500 companies, but generic ChatGPT is amazing at everything and perfect at nothing. When you need domain-specific accuracy, cost control, or data privacy that vanilla models can't deliver, training your own becomes essential. This guide covers the practical methods, tools, and step-by-step process to train a GPT model that understands your specific use case.

Individual AI agents are powerful, but their true value is unlocked when they operate cooperatively as a collective. This coordinated effort, known as AI agent orchestration, is fundamental to creating truly autonomous systems capable of managing intricate, multi-step business processes. This guide will walk you through the core patterns of AI agent orchestration and build a practical, autonomous agent using the robust, low-code combination of n8n and Decodo MCP.