Welcome to Decodo Blog!

AI web scraping with Python lets you extract data from websites without relying on fragile parsing rules. AI helps handling page inconsistencies and dynamic content, while Python continues to manage fetching. In this guide, you'll see how models extract data from unstructured pages, reduce manual parsing rules, support automation, and scale into reliable pipelines.

Wikipedia has over 60 million articles, making it a valuable resource for machine learning training data, research datasets, and competitive intelligence. This tutorial guides you through extracting your first article to building crawlers that navigate Wikipedia's knowledge graph. You'll learn to extract titles, infoboxes, tables, and image references, then scale up to crawling entire topic clusters.

You've mastered static HTML scraping, but now you're staring at a site where Requests + Beautiful Soup returns nothing but an empty <div> and <script> tags. Welcome to JavaScript-rendered content, where you get the material after the initial request. In this guide, we'll tackle dynamic sites using Python and Selenium (plus a Beautiful Soup alternative).

AI web scraping is the process of extracting data from web pages with the help of machine learning and large language models. It uses them to read a web page the same way humans do, by understanding its meaning. The problem with traditional scrapers is that they tend to stop working when the HTML structure is inconsistent or incomplete. In these cases, AI helps scrapers to quickly adapt and find the right information. Sometimes, even a single misplaced tag can ruin your whole web scraping run. AI solves that by shifting focus to the meaning of the content rather than relying on rigid rules to define what data to scrape. That's why AI web scraping is becoming a practical choice for many projects.

SoundCloud is a mother lode for AI training data, with millions of audio tracks spanning every genre and style imaginable. In this guide, we’ll show you how to tap into that library using Node.js, with the help of proxies. You’ll get hands-on code examples and learn how to collect audio data for three key AI use cases: music generation, audio enhancement, and voice training.

Web scraping with Ruby might not be the first language that comes to mind for data extraction – Python usually steals the spotlight here. However, Ruby's elegant syntax and powerful gems make it surprisingly effective. This guide walks you through building Ruby scrapers from your first HTTP request to production-ready systems that handle JavaScript rendering, proxy rotation, and anti-bot measures. We'll cover essential tools like HTTParty and Nokogiri, show practical code examples, and teach you how to avoid blocks and scale safely.

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.

The morning after Black Friday, Sarah Chen had buyer's remorse. She'd grabbed a skincare set from Ulta on Friday for $14, thinking she'd scored a deal. But by Monday, she watched the same product drop to $12. By Wednesday, it hit $11. She'd paid 27% more than patient shoppers who waited just three days.

An exclusive analysis conducted by web data company Decodo, with data collected starting from Black Friday (28 November 2025) through early December 2025, exposed a phenomenon where the days after Black Friday matter as much as the day itself. For millions of shoppers, the question isn't whether they got a good deal on November 28. It's whether they should have waited until December.

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.

The talk around whether the UK government could put a ban on ticket resale above face value is set to reshape the live events industry, from secondary ticketing platforms to artists and venues, with fan fairness at the center of the new rules. Decodo, a leading web data collection provider, has commented on the latest proposals to reform the ticket resale market, revealing the impact on key stakeholders.

Cloudflare is experiencing a global outage affecting hundreds of millions of users worldwide. Major platforms, including X and OpenAI, are impacted by it. This article breaks down the current Cloudflare status, what's causing widespread 500 errors, and actionable steps for users and website owners dealing with Cloudflare downtime.

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.

As the biggest shopping festival of the year approaches, a dynamic shift occurs across thousands of online storefronts. Prices fluctuate by the hour, algorithms compete for consumer attention, and shoppers equip themselves with AI-powered price trackers and browser extensions. Black Friday has grown from a single-day shopping event into a months-long strategic tango between retailers and increasingly savvy consumers.

An exclusive analysis by web data company Decodo of 150K+ price observations across 37 major US eCommerce retailers, tracked throughout the year, reveals the complex strategies both sides deploy. The data paints a picture of a retail ecosystem in constant flux, where timing, category selection, and pricing algorithms can mean the difference between a genuine deal and clever marketing tricks.

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.