Most developers who integrate the Minexa API use it the same way: train a scraper, pass some URLs, get structured JSON back. That covers the basics. But there is a wider set of capabilities built into the API that rarely gets used, either because it is not obvious from the docs or because the default setup already works well enough that no one goes looking further. This article covers ten of those capabilities, with enough detail to know when each one is worth reaching for. 1

Every piece of data you have ever needed from a website was already sitting there, visible on the screen. The problem was never access. It was format. Web pages are built to be read by humans, not processed by spreadsheets. The information is real, it is current, and it is public. But it lives inside a visual layout designed for a browser, not inside the rows and columns your analysis needs. That gap between what you can see and what you can actually use is where most data co

Most data collection problems do not announce themselves. A field returns the wrong value. A column silently pulls from the wrong section of the page. A pipeline runs without errors but the output is unusable. By the time the issue surfaces, the damage is already in the dataset. This post walks through the specific points where unstructured data collection breaks, and explains what a structured approach actually does differently at each stage. Breakdown 1: Capturing data from

You send a request. The response comes back empty. No error, no explanation, just nothing where your data should be. This is one of the most common frustrations in data extraction pipelines, and it almost always traces back to one of a handful of technical barriers that websites put in place. The question is not whether these barriers exist. They do, on most sites worth scraping. The question is how your extraction layer handles them. This post answers the specific questions

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

OLX is one of Eastern Europe's largest classifieds platforms. Its real estate section for Ukraine lists thousands of houses, apartments, and land plots updated daily. Getting that data into a structured format without writing a custom scraper from scratch is where the Minexa API workflow saves significant time. This guide walks through extracting house sale listings from OLX's Lviv region page using Minexa. Step 1: Train a scraper with the Chrome extension Before calling the

You can see the data. It is sitting right there on the page: prices, listings, contact details, job postings, product specs. But getting it out in a usable format means copying row by row, writing brittle selectors that break on the next deploy, or feeding pages into an LLM and hoping the output is consistent. None of these hold up past a few hundred pages. This is the actual problem with web data collection today. It is not that the data is hidden. It is that every path to e