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

Credit card benefit data buried inside accordion-based FAQ pages is some of the hardest structured content to extract reliably. Capital One's rental car insurance help page is a good example: each question expands into a body containing card eligibility lists, benefit administrator contacts, and coverage exclusions, all encoded inside Angular components with dynamic display states. This guide shows how to pull that content into a clean, repeatable data pipeline using the Mine

Job market data from Instahyre is genuinely useful: role titles, required skills, company profiles, founding years, and direct application links all sitting on a structured listing page. The challenge is getting it out in a form you can actually work with at scale. This guide walks through how to extract that data programmatically using the Minexa API, covering the full workflow from training a scraper in the browser to calling the API and receiving clean, structured JSON. Wh

Here is what the Minexa API returns when you point a trained scraper at the Australian Taxation Office tax rates and codes page. Start with the data, then work backward through how it was built. What the extracted data looks like Below are three rows from a live extraction of ato.gov.au/tax-rates-and-codes. Fields prefixed with meta__ are removed for clarity. [ { "announcement_dates": "24 June 2026", "element_class": "", "important_dates": [{"value": "24 June 2026", "tag": "d

SEC filings are public, structured, and updated regularly. But pulling them into a usable dataset from Eli Lilly's investor portal manually is slow work. This walkthrough shows how to build a repeatable extraction pipeline using the Minexa API. The scenario: monitoring Eli Lilly SEC filings at scale Imagine you need to track every new filing that appears on the Eli Lilly SEC filings page and feed that data into a downstream system automatically. You need filing types, dates,

Media monitoring used to mean hiring analysts to read through dozens of publications every morning and manually log what they found. That approach does not scale, and it does not stay current. When a story breaks across multiple outlets at once, the window to act on it is short. Structured news data changes that equation entirely. With the right extraction setup, a monitoring firm can pull article titles, publication timestamps, author names, section tags, and summary text fr

Kleinanzeigen is Germany's largest classifieds platform, and its land and garden listings category contains thousands of active property ads updated daily. Getting that data into a structured format manually is not practical at any meaningful scale. This guide shows how to do it programmatically using the Minexa API. The workflow has two tracks. You run track one once in the browser to train the scraper. You run track two as many times as you need via the API. Once track one

Flight route data from Kayak is genuinely useful for travel research, price monitoring, and competitive analysis. This guide walks through exactly how to extract it using the Minexa.ai Chrome extension, no code required. Watch the full tutorial first Before going through the steps below, watch the video walkthrough. It covers the entire extraction from start to export in a single session. Checkpoint 1: open Minexa.ai and navigate to Kayak Install the Minexa.ai Chrome extensio