Collecting job listing data from Doda, one of Japan's largest job platforms, by hand is slow and breaks down quickly once you need more than a handful of records. Each company card on Doda surfaces multiple job references, salary structures, and linked detail pages, and copying that manually across hundreds of pages is not a realistic workflow. This guide walks through exactly how to extract that data using the Minexa.ai Chrome extension, no code required, starting from the c

Repository listing pages are some of the most information-dense pages on the web for developers. Hundreds of projects, each with stars, forks, language tags, contributor counts, license types, and last-commit timestamps, all sitting in a structured list that updates constantly. The problem is that none of that data is accessible in a usable format without either clicking through it manually or writing a scraper from scratch. This post covers how to extract that data at scale

The OSPI Online Learning Course Catalog lists every state-approved online course available to Washington K-12 students, including provider names, grade eligibility, course levels, and direct links to provider PDFs. Pulling that data manually means clicking through paginated tables and copying rows one by one. The Minexa API removes that entirely. Watch the full extraction walkthrough before diving into the steps below. What the extracted data looks like Here is a sample of wh

Every job listing on Freshersworld contains more structured data than it appears to hold at first glance. Behind each card sits a salary range, a relative posting timestamp, a numeric job ID embedded in a CSS class, and a full list of eligible qualifications packed into a single traversable array. Getting all of that out cleanly, at scale, without writing selectors or maintaining parsing logic, is exactly what the Minexa API is built for. This guide walks through the complete

Air quality data is publicly available on AQICN, but getting it into a structured format that a pipeline can actually use is a different problem. The page renders pollutant readings dynamically, attribution strings span multiple lines, and each row carries embedded chart images encoded as Base64. None of that is accessible through a simple HTTP request. This guide walks through how to extract that data programmatically using the Minexa API, covering the full workflow from scr

Federal job listings contain more structured data than most people realize. USAJobs surfaces job titles, hiring organizations, salary ranges, employment types, application deadlines, and eligibility paths all on a single search results page. The challenge is getting that data out in a usable format without copying rows manually. This guide walks through how to extract that data using the Minexa.ai Chrome extension, a no-code tool that turns any structured web page into a down

Collecting property listing data from Dom.ria.com manually means opening dozens of pages, copying prices, addresses, and specs one by one, and ending up with a spreadsheet that is already outdated by the time you finish. This post shows how to replace that process with a single API call using Minexa, a web data extraction platform that turns any structured listing page into clean JSON without writing selectors or parsing HTML by hand. Before: what you are dealing with on Dom.

Clinical trials data is one of the most structurally rich and time-sensitive categories of public research information available on the web. Fred Hutch Cancer Center publishes a continuously updated directory of active trials at fredhutch.org, covering oncology, hematology, behavioral interventions, and surgical comparisons across phase I through phase III studies. Extracting that data programmatically opens up a range of research intelligence workflows that are difficult to

Ryanair publishes a public flight listing page that shows available routes, departure dates, origin and destination cities, IATA airport codes, one-way fares, and direct booking links. All of it is visible in the browser. None of it is easy to collect at scale without a tool built for the job. This guide covers how to extract that data using Minexa.ai, a Chrome extension that detects page structure automatically and exports results to Excel, Google Sheets, or JSON without any

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

Kavak is one of Latin America's largest used car marketplaces, and its Brazilian listings page at kavak.com/br/seminovos contains hundreds of vehicle cards updated regularly with price, mileage, model year, location, and direct links to individual detail pages. Extracting that data at scale without writing custom scraper code is exactly what the Minexa API is built for. This walkthrough covers the full workflow: training a scraper once using the Minexa Chrome extension, then

Built In is a tech-focused jobs and company directory that covers a wide range of industries, including agriculture and food production. Its company listings page at builtin.com/companies/type/agriculture-companies surfaces company names, employee counts, office locations, benefits counts, industry tags, and hiring status all in one place. That makes it a useful starting point for agri-food market research, competitive benchmarking, or building a targeted outreach list. The c

The T-Bank invest portal lists dozens of ETFs and investment funds on a single paginated page. Each card carries a fund name, a logo, a performance chart rendered in SVG, and a link to the fund detail page. Visually, it reads cleanly. Programmatically, it is a different story: the chart data is embedded in SVG polyline attributes, gradient identifiers are scattered across path elements, and performance direction is encoded in CSS variable names rather than plain text values.

Michael Page publishes hundreds of active job listings across London every day. If you want that data in a spreadsheet, structured and ready to analyse, the Minexa.ai Chrome extension gets you there without writing a single line of code. This walkthrough covers the full extraction process from the michaelpage.co.uk/jobs/london page, showing exactly what you see at each step and what to do. Watch the full tutorial first The video below covers the complete workflow from install

GitHub Explore surfaces what the developer community is actually building right now. Trending repositories, active topics, star counts, programming languages, and full card-level metadata are all sitting on a single public page. The challenge is getting that data into a structured format you can query, filter, and feed into a pipeline. This guide walks through how to extract that data programmatically using the Minexa API. The workflow has two phases: train a scraper once usi

The data below was extracted from the Visit Fort Wayne restaurant directory in a single API call. Before explaining how the pipeline works, here is what the output looks like. What the extracted data looks like Each row in the response corresponds to one restaurant listing on the Visit Fort Wayne page. Here are three labelled records from the extraction: [ { "restaurant_name": "Wagyu Burger Shack", "listing_url": "/listing/wagyu-burger-shack/55389/", "external_link":...

Fleet procurement teams and logistics analysts working in Brazil regularly need structured vehicle specification data to compare configurations, validate supplier quotes, and build internal reference datasets. Mercado Livre publishes detailed catalogue pages for commercial vehicles, including full technical comparison tables per trim, but that data sits inside a web page rather than a spreadsheet. Getting it out manually, row by row, is slow and error-prone at any meaningful

Most people who need web data for a work project assume the task is either trivially easy or completely out of reach. Neither assumption tends to be accurate, and both lead to the same outcome: hours spent doing manually what a tool could handle in minutes. Here are ten things that commonly trip people up before they even get started. 1. Multi-level navigation means the site cannot be scraped Some sites require you to click through several layers before reaching the actual da

Government funding portals publish structured competition data in plain HTML, yet collecting it at scale means clicking through pages manually or writing brittle scraping scripts that break whenever the layout shifts. The Apply for Innovation Funding service, part of the UK government's service.gov.uk platform, lists open and upcoming Innovate UK competitions with details that matter to grant consultants, R&D teams, and business intelligence analysts. This guide shows how to

Flight deal pages update constantly. Prices shift by the hour, routes appear and disappear, and the window to act on a good fare is short. Collecting that data manually, row by row, is not a realistic option if you want anything beyond a snapshot. This guide walks through how to extract structured flight deals data from CheapOair using the Minexa.ai Chrome extension — no code, no configuration files, no prior scraping experience needed. Watch the full tutorial first Before go