Collecting product data manually is one of those tasks that feels manageable until it isn't. A few pages, a spreadsheet, some copy-pasting. Then the product catalog grows, the sites multiply, and suddenly what took an afternoon now takes a week. This post answers the questions that come up most often when people start thinking seriously about automating ecommerce data collection. Why does manual product data collection break down? The core problem is volume. Browsing through

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

Salary data is publicly available on dozens of platforms. The problem is not access. The problem is that reading it page by page, role by role, does not scale. Compensation researchers who need structured, comparable data across hundreds of roles end up spending most of their time copying and formatting rather than actually analysing. This guide covers how to extract salary data from role detail pages using the Minexa.ai Chrome extension, what fields you can expect to capture

Drug label data sitting behind a browser is useless to a pipeline. This guide shows how to pull structured pharmaceutical listings from PDR at scale using the Minexa API, covering every field worth knowing and the exact request body to get started. What PDR exposes at the browse-by-drug-name endpoint PDR (pdr.net) is a clinical reference platform used across healthcare and biotech. Its drug browse page at pdr.net/browse-by-drug-name lists thousands of drug entries alphabetica

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.

Travel Wisconsin's food and drink directory lists hundreds of restaurants, bars, breweries, cafes, and distilleries across the state. If you want that data in a spreadsheet rather than clicking through pages one by one, the Minexa.ai Chrome extension handles the full extraction without any code. Here is the complete walkthrough, step by step. Watch the full tutorial first The video below covers the entire workflow from installing the extension to downloading your export. Watc

Queensland.com lists hundreds of tours, activities, and experiences across the state. Each listing card carries a company name, a full activity description, a cover image, booking links, and a unique product identifier. Getting all of that into a structured dataset manually is not realistic at any meaningful scale. This walkthrough shows how to extract that data using the Minexa API. You train a scraper once through the Minexa Chrome extension, then call the API programmatica

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

Metacritic's game browse page is one of the most complete public sources of critic-scored game data on the web. Every listing carries a Metascore, an ESRB rating, a release date, a full description, and a direct link to the game's detail page. If you are building a game analytics pipeline, a content recommendation engine, or a media intelligence feed, this is a dataset worth having in structured form. This guide walks through how to extract that data at scale using the Minexa

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

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

Government procurement portals publish some of the most consistently structured public data available online. OregonBuys, Oregon's centralized public purchasing system at oregonbuys.gov, lists open bids from state agencies, counties, school districts, tribal organizations, and public utilities all in one place. The data is public, updated regularly, and genuinely useful for vendors tracking opportunities, researchers monitoring public spending, and analysts building procureme

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

Website traffic data is one of the most useful signals for competitive research, SEO benchmarking, and market sizing. SE Ranking publishes traffic estimates for domains directly on its website traffic checker page, and with the Minexa.ai Chrome extension, you can pull that data into a structured spreadsheet without writing a single line of code. This walkthrough shows you exactly how to do it, step by step, with screenshots at each stage so you know what to expect. Watch the

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

The IMO's GISIS portal is one of the most comprehensive public sources of maritime regulatory data on the internet. Ship particulars, maritime security records, casualty reports, treaty statuses, port reception facilities, ballast water management data, and more than twenty other module categories are all publicly accessible from a single index page. The challenge is not finding the data. It is getting it out in a form you can actually use programmatically. This guide covers