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

Clinical research data published on institutional websites is some of the most structured, high-value information available publicly. Extracting it at scale, however, has traditionally required either manual copy-paste or fragile custom scrapers that break the moment a page layout changes. This guide shows how to extract clinical trial listings from UCSD Clinical Trials (the official trial browser at clinicaltrials.ucsd.edu/browse/) using the Minexa API — a data extraction pl

VesselFinder tracks hundreds of thousands of ships worldwide. The vessel list at vesselfinder.com/vessels is one of the most complete public directories of maritime assets available, covering cargo ships, tankers, cruise liners, and more. Getting that data into a spreadsheet for analysis, research, or fleet monitoring normally means a lot of manual copying. This tutorial shows how to do it automatically using the Minexa Chrome extension. What data you can extract from VesselF

Collecting job market data from Jora manually is slow, inconsistent, and breaks the moment you need more than a handful of listings. The Minexa API gives developers a repeatable, structured extraction pipeline that works across thousands of pages without writing a single selector. This guide covers the full workflow: training a scraper on Jora, retrieving your scraper_id, and calling the API to extract job listings at scale. What data Jora exposes Each listing on a Jora searc

Most data extraction pipelines start the same way: someone needs structured data from a website, and the first instinct is to write a scraper. Then comes the selector logic, the edge cases, the JavaScript rendering layer, the proxy setup, and eventually a fragile script that breaks when the site updates. The Minexa API was built to replace that entire process with a single trained scraper and a POST request. This guide walks through exactly how that works, from training your