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

Alternative.to lists software alternatives organized by category, platform, and language. Every listing card contains a product name, description, external website link, feature count, social profiles, and platform metadata. Pulling that data manually across dozens of pages is slow. The Minexa API makes it programmable. The workflow has two phases: train a scraper once using the Minexa Chrome extension, then call the API to extract data at scale from any number of Alternative

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

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

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

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

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

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

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

Seek is Australia's largest job board. If you are tracking entry-level hiring trends, monitoring which companies are recruiting in regional areas, or building a structured jobs dataset for research or analysis, the listings on Seek contain exactly the data you need. The challenge is that it sits inside job cards on a paginated search results page, not in a downloadable file. This guide shows how to extract that data using Minexa.ai, a web data extraction tool with a Chrome ex

The Companies House SIC code list is a complete reference of Standard Industrial Classification codes used by UK companies when registering or filing. Every active company on the register is assigned at least one SIC code, making this page a foundational lookup table for anyone building company intelligence pipelines, sector filters, or compliance tooling. This guide walks through how to extract the full SIC code dataset from resources.companieshouse.gov.uk/sic/ using the Min

OLX India publishes thousands of active property listings daily, covering rentals, sales, PG accommodations, and commercial spaces across every major Indian city. For a developer building a property market dataset, that volume is exactly the challenge: the data is all there, publicly visible, but extracting it page by page manually is not a realistic option at scale. This walkthrough follows a concrete scenario: setting up a repeatable pipeline that pulls structured property