Event pages are full of structured, actionable data. Dates, venues, speaker lineups, ticket tiers, organiser details, topic categories. For marketing platforms, that data is the raw material for campaign targeting, content planning, and partner identification. The problem is that it sits locked inside HTML, spread across thousands of individual event URLs. This guide explains how to extract it at scale using the Minexa API, starting from a single trained scraper and ending wi

OpenRent is one of the UK's largest direct-to-landlord rental platforms. Its West London listings page surfaces rental prices, deposit amounts, availability dates, bedroom counts, furnishing status, and direct property URLs, all in a consistent list structure that maps cleanly to a structured dataset. This guide covers how to extract that data at scale using the Minexa API, a deterministic web extraction platform that replaces manual selector writing with a one-time visual tr

Marketplace listing pages contain exactly the data that price monitoring tools are built around: current price, original price, discount percentage, seller name, availability status, shipping cost, and condition. The problem is not that the data is hidden. It is visible on every page. The problem is extracting it consistently across thousands of listings, across multiple sources, without the extraction layer introducing errors or requiring constant maintenance. This post walk

Most people who try web scraping for the first time give up before they collect a single useful row of data. Not because the data is not there. It is right there on the screen. The problem is everything that sits between the visible page and a clean, usable spreadsheet. This post is about that gap, what causes it, and what it actually takes to close it. The problem looks smaller than it is When someone decides to start collecting web data without a technical background, the f

You open a website. The data you need is right there, laid out in rows, clearly labeled, exactly what your project requires. Then you realize you have to get it out of the page and into a spreadsheet, and that is where things stop being simple. Copying row by row is not realistic if there are hundreds of results. Building a scraper requires knowing how the site is structured in code. Hiring someone to do it takes time and budget you may not have. And if the site updates its l

You are looking at a page full of exactly the data you need. Prices, job titles, company names, property listings. It is all there, visible, organized, right in front of you. And yet getting it into a spreadsheet where you can actually use it means either copying it by hand or calling someone who knows how to write code. That gap between 'the data exists' and 'the data is usable' is where most people get stuck. And it is not because the problem is hard. It is because the tool