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

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

Indonetwork is one of Indonesia's largest B2B directories, listing thousands of verified companies across industries including cargo, freight forwarding, customs brokerage, and domestic logistics. The cargo and logistics section alone contains hundreds of active company profiles, each with addresses, service descriptions, contact data, and membership tier information. If you are building a supplier database, researching logistics partners across Indonesian cities, or monitori

FRED publishes hundreds of commodity price series, all publicly accessible, all updated regularly. The problem is that the data sits inside a category listing page that was built for browsing, not for export. Getting it into a spreadsheet means either copying rows one by one or building something custom. Neither option scales. This guide shows how to extract structured commodity price data from fred.stlouisfed.org using the Minexa.ai Chrome extension, no code required. Watch

You found the price on the page. You can see it. You just cannot get it into your spreadsheet reliably. This is one of the most common friction points for anyone trying to build a vendor comparison sheet or track prices across multiple suppliers. The data is right there, visible in the browser, but every formula you try either pulls the wrong number, returns an error, or works once and then silently breaks. Understanding why this happens is the first step to fixing it properl

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

A Greenhouse job board page looks clean in a browser. Open the raw source and it is a different story: nested divs, dynamic class names, carousel wrappers, and image paths buried inside attributes. Getting that into a spreadsheet manually takes far longer than it should. This guide shows how to go from that raw page to a structured, exportable dataset using Minexa.ai, a Chrome extension that detects and extracts data from any web page without requiring any code. What the page

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,

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

OpenRent is one of the UK's largest direct-to-landlord rental platforms. Its West London listings page surfaces rental prices, deposit amounts, availability dates, bedroom counts, furnishing status, and direct property URLs, all in a consistent list structure that maps cleanly to a structured dataset. This guide covers how to extract that data at scale using the Minexa API, a deterministic web extraction platform that replaces manual selector writing with a one-time visual tr

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

OpenEI hosts one of the most comprehensive collections of publicly available energy and environmental datasets. The data is there, clearly listed, but getting it into a spreadsheet or a structured file requires clicking through pages and copying entries one by one. This walkthrough shows how to extract that data automatically using the Minexa.ai Chrome extension, with no code required. Watch the extraction in action Before going through the steps, watch the full tutorial belo