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TL;DR: A web crawler is a program that systematically navigates the web by following links from page to page. Python is the go-to language for building crawlers thanks to libraries like Requests, Beautiful Soup, and Scrapy. This guide covers everything from your first 50-line crawler to a production-grade Scrapy setup with proxy integration, JavaScript rendering, and distributed architecture. If you've ever had to collect data from hundreds or thousands of pages and done it manually, this is for you.

Scrapy is a powerful web scraping framework available in Python. Its asynchronous architecture makes it faster than sequential scrapers built with Requests or Beautiful Soup, and it includes everything needed for production-ready scraping: spiders, items, pipelines, throttling, retries, data export, and middleware. In this guide, you'll learn how to set up Scrapy, build and customize spiders, handle pagination, structure and store data, extend Scrapy with middlewares and proxies, and apply best practices for scraping at scale.

SOCKS5 proxies route traffic at the network level and work with a wide range of applications. This guide shows how to set up and use a SOCKS5 proxy across devices and apps. You’ll learn how to configure SOCKS5 on Windows, macOS, Linux, mobile devices, browsers, and selected applications, and when system-wide or application-level setup is the better choice.

AI-powered automation has moved from niche tooling to a 2026 priority. Tools such as OpenClaw turn large language models into proactive messaging assistants, while n8n offers a visual workflow builder for connecting apps and orchestrating processes. This guide compares their architecture, AI capabilities, pricing, and best-fit use cases so you can quickly decide which tool matches your needs.

Every time you check the weather, order food, or scroll through social media, APIs are working behind the scenes to make it happen. An API (Application Programming Interface) is essentially a set of rules that lets different software applications talk to each other and share data without you ever noticing. In this guide, you'll learn more about what they are, how they work, and even get the chance to create your own.

Manual eCommerce data collection breaks because the data doesn’t stay stable. Prices change daily, products disappear and reappear under the same URL, and even mid-sized stores list tens of thousands of SKUs. On top of that, much of the content is rendered with JavaScript, layouts shift due to constant A/B testing, and anti-bot systems detect repeated automated access. This guide shows you how to analyze a target site and choose the right extraction approach.

Not all proxies actually protect your identity. Some proxies openly tell websites, "This request came through a proxy," while some can even leak your real IP if they're configured poorly. There are three proxy anonymity levels: transparent, anonymous, and elite. This article will walk you through each one, explain how they work, and help you choose the most suitable option for you.

Web scraping used to mean wrestling with Python scripts, brittle selectors, and an ever-growing list of sites that suddenly stopped working. AI-powered agents are changing that. This guide walks you through setting up OpenClaw with Decodo's scraping skill to build a reliable, proxy-backed web scraping setup. By the end, you'll have a working configuration that handles JavaScript rendering, bot detection, and proxy rotation out of the box.

Dynamic pricing is everywhere – from the flight you booked last week to the headphones in your cart right now. But as AI-powered algorithms grow more sophisticated, adjusting prices millions of times a day based on demand, competition, and increasingly personal data, a critical question is emerging – is this still fair? We analyzed over 1.5M eCommerce price changes and introduced our second-annual Dynamic Pricing Index to find out what’s really happening beneath the surface.

Dynamic pricing, the practice of adjusting product prices in real time based on demand, competition, inventory, and other market signals, has become one of the defining features of modern eCommerce. Once limited to airline tickets and hotel rooms, algorithmic pricing now touches nearly every product category, from groceries and fashion to electronics and home improvement supplies. Our second-annual Dynamic Pricing Index, compiled from over 1.5M data points tracked at four-hour intervals across more than 120 eCommerce platforms in 40+ countries, reveals the scale and sophistication of this practice.

The US and Asia dominate global eCommerce, but their dynamic pricing strategies reveal distinct philosophies. Using 1.5M data points from 120+ platforms, our second-annual Dynamic Pricing Index compares how each region approaches price changes. We analyzed discount depth, pricing volatility, and stability to uncover what these patterns reveal about consumer behavior and competitive strategy.

There's a reason that a flat-screen TV costs $999 instead of $1,000. That grocery item rings up at $4.99, not $5.00. Your new sneakers are priced at $79.95, conveniently avoiding the psychological cliff of $80. It's called psychological pricing, and according to our Dynamic Pricing Index report of over 1M price observations on 120 eCommerce platforms, it's not just alive and well in eCommerce. It's absolutely everywhere.

Real-time pricing has moved from a competitive advantage to a competitive necessity. Retailers without algorithmic pricing capabilities now face a structural disadvantage against competitors who can respond to market conditions in minutes rather than days. This report provides the benchmarking data your pricing, merchandising, and technology teams need to assess your competitive position and inform investment decisions.

If you’ve ever added a product to your cart only to find the price has shifted by the time you check out, you’ve experienced dynamic pricing in action. What once seemed like sporadic discounting is now a sophisticated, algorithmically driven discipline that reshapes prices across millions of products every hour of every day.

Scraping projects usually start simple: a Python script, the Beautiful Soup parsing library, and a list of URLs. That's enough for small jobs. Once you're past a few hundred thousand pages, you start hitting problems: timeouts, IP bans, parsers returning empty fields because someone changed a div to a span. At that point, it's not a coding problem anymore, it's an infrastructure problem. This guide covers the architecture, proxy management, anti-bot evasion, pipelines, costs, compliance, where the industry is headed, and build vs. buy decisions.

TL;DR: A proxy server acts as an intermediary between your device and the internet, masking your IP address and routing your requests through alternative IPs. Businesses use proxies to bypass geo-restrictions, avoid blocks and CAPTCHAs, scrape web data at scale, verify ads, monitor prices, and maintain online anonymity. With the proxy market projected to reach USD 3.12 billion by 2032, understanding which proxy type fits your needs can make the difference between seamless data collection and constant roadblocks.

Whether you're running an SEO audit, planning a site migration, or hunting down broken links, there's one task you'll inevitably face – finding every URL on a website. It sounds simple, but it isn't. Search engines don't index everything, sitemaps are often outdated, and dynamic pages hide behind JavaScript. This guide walks you through every major discovery method, from quick Google search operators and no-code scrapers to custom Python scripts.

The data scraping tools market is growing significantly, valued at approximately $875.46M in 2026. The market is projected to grow more due to the increasing demand for real-time data collection across various industries.