QuintoAndar publishes thousands of property listings across Brazilian cities, updated continuously. If you are building a dataset of house prices, neighborhood trends, or listing volumes in Rio de Janeiro, pulling that data manually is not realistic at any meaningful scale. This guide walks through how to extract structured listing data from QuintoAndar using the Minexa API — a two-phase workflow where you train a scraper once visually, then call it programmatically as many t

Cutshort lists hundreds of recruitment and HR companies, each with funding status, headcount bands, founding year, and a full company description. Collecting that data page by page manually is not a realistic option at any meaningful scale. This guide shows how to train a scraper once using the Minexa Chrome extension and then call the Minexa API to extract it programmatically. Watch the full walkthrough The video above covers every step from opening Minexa to running the ext

OLX India publishes thousands of active property listings daily, covering rentals, sales, PG accommodations, and commercial spaces across every major Indian city. For a developer building a property market dataset, that volume is exactly the challenge: the data is all there, publicly visible, but extracting it page by page manually is not a realistic option at scale. This walkthrough follows a concrete scenario: setting up a repeatable pipeline that pulls structured property

Collecting clinical trial data by hand means opening dozens of pages, copying fields one by one, and hoping nothing changes between sessions. There is a faster way. The UCSF Clinical Trials browse directory lists active studies across conditions, phases, and study types. It is a structured, publicly accessible source that researchers, analysts, and health data teams regularly need in bulk. The problem is that the site was not built for export. Getting the data out in a usable

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

Collecting job listings at scale sounds straightforward until you actually try to do it. The data is right there on the page. Hundreds of fields per listing, thousands of companies, millions of postings updated daily. The problem is not access. The problem is that getting that data into a structured, usable format requires solving a set of technical problems that compound quickly as volume increases. Anyone who has attempted to build a job data pipeline from scratch knows wha

Comcast files regularly with the SEC, and all those filings are listed publicly on cmcsa.com. The page covers everything from 8-K material event reports to proxy statements, Form 4 ownership changes, and Schedule 13G disclosures. Getting that data into a structured format manually means copying row by row. Here is what the extracted output actually looks like, and how to get it in minutes using the Minexa.ai Chrome extension. What the extracted data looks like Each row in the

Preqin publishes structured updates about private markets, fund forecasts, credit ratings, and sustainability classifications. Analysts who track these updates manually visit the page, read through articles, and copy what they need. That works for one article. It breaks down when you need to monitor dozens of updates across weeks or months. The Minexa API solves this by letting you train a scraper once on the Preqin help center articles page, then call it programmatically whe

Eventbrite lists thousands of online webinars at any given time. Collecting that data manually, event by event, is slow and does not scale. This guide shows how to extract structured webinar listings from Eventbrite using Minexa.ai, a no-code Chrome extension that turns any page into a structured dataset in minutes. What you get out of the extraction Before walking through the steps, here is a quick checklist of what Minexa pulls from the Eventbrite webinar listing page: Even

Your scraper worked perfectly in testing. Clean responses, correct fields, no errors. Then you pushed it to production and within two weeks it was returning empty results, burning through credits five times faster than expected, or silently failing on the exact pages you needed most. This is not a rare edge case. It is one of the most common patterns in web data collection, and it happens because testing conditions and production conditions are fundamentally different environ

Basic scraping scripts stopped working reliably years ago. The question is not whether web scraping is dead. It is whether the approach you are using was ever going to survive contact with modern infrastructure. If your requests-based script gets blocked by Cloudflare or a WAF, that is not a scraping problem. That is a browser fingerprinting problem. Sites no longer just check whether your IP is on a blocklist. They check whether your request looks like it came from a real br

Every week, people post requests asking developers to pull data from public websites. The requests are specific: permit volumes by city, trend breakdowns by region, structured output in CSV or spreadsheet format. The person posting knows exactly what they want. They just do not know how to get it without hiring someone. That gap between knowing what data you need and being able to collect it yourself is where a lot of time and money quietly disappears. And in most cases, the

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

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

Public records data is technically available to anyone. The problem is that collecting it in a usable format takes far longer than it should. GovTrack publishes detailed legislative information including bills, U.S. Code references, and congressional activity, but that data sits inside web pages rather than downloadable files. Copying it row by row is not a realistic option when you need structured output at any meaningful scale. This guide shows how to extract structured dat

Every hour spent copying data from a webpage by hand is an hour that produces nothing reusable. The data sits in a spreadsheet, the method lives in no one's head in particular, and the next time the same data is needed, the process starts over from scratch. This is not a niche problem. It shows up across research teams, operations teams, sales teams, and product teams alike. The data they need is publicly visible on the web. Getting it into a usable format is where the time g

Uber Eats city pages list every restaurant available for delivery in a given area, complete with ratings, cuisine types, delivery times, and promotional labels. That data is publicly visible but entirely unstructured. Pulling it manually is slow and does not scale. This guide shows how to extract it programmatically using the Minexa API. What the data looks like Before walking through the extraction steps, here is a sample of the structured output Minexa returns from an Uber

The NC.gov grant opportunities page lists every active state grant program in one place, organized by category. Getting that data into a spreadsheet manually means copying row by row across dozens of programs. With the Minexa.ai Chrome extension, the whole page becomes a structured dataset in a few minutes. Here is exactly what the process looks like, step by step. What data you get Each row on the NC.gov grant opportunities page contains four fields Minexa extracts cleanly:

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

Wise is best known as a money transfer platform, but it also hosts a publicly accessible stock directory covering thousands of listed companies across global exchanges. Each category page on wise.com lists company names, ticker symbols, and links to individual stock detail pages. If you need that data in a structured format for research, financial analysis, or portfolio tooling, copying it manually is not realistic at any meaningful scale. This guide shows how to extract that