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

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

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

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

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

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

Non-profits and NGOs tracking federal funding opportunities spend considerable time manually checking Grants.gov for new postings. Building a structured pipeline from simpler.grants.gov/search changes that entirely: every new opportunity, deadline, and award amount becomes queryable data your systems can act on automatically. This guide walks through how to build that pipeline using the Minexa API, starting from scraper training in the Chrome extension through to a running Py

The TIOBE Index is one of the most referenced sources for tracking programming language popularity over time. It aggregates search engine query data across dozens of sources and publishes a ranked list of languages monthly. For developers building tooling dashboards, curriculum platforms, or hiring signal pipelines, having programmatic access to that ranking data is more useful than checking the page manually each month. This guide walks through how to extract structured rank

Trustpilot holds millions of customer reviews across thousands of brands. If you want that data in a structured, queryable format, copying it manually is not a realistic option. This post shows how to build a repeatable extraction pipeline pulling review data from Trustpilot using the Minexa API. The workflow has two phases: train a scraper once using the Minexa Chrome extension, then call the API programmatically to extract reviews at scale across any number of pages. Step 1

If you are building a job market intelligence pipeline for the Canadian market, Simply Hired is a solid data source. The simplyhired.ca Toronto listings page surfaces entry-level roles across dozens of industries, updated regularly. Getting that data into a structured format, reliably and at scale, is what this guide covers. The workflow uses the Minexa API: train a scraper once using the Minexa Chrome extension, then call the API programmatically to extract listings whenever

Collecting job listings at scale sounds straightforward until you actually try to do it. The data is right there on the page. Hundreds of fields per listing, thousands of companies, millions of postings updated daily. The problem is not access. The problem is that getting that data into a structured, usable format requires solving a set of technical problems that compound quickly as volume increases. Anyone who has attempted to build a job data pipeline from scratch knows wha

OpenCorporates publishes one of the most comprehensive indexes of company registers available publicly. The registers page at opencorporates.com/registers lists official corporate filing sources from jurisdictions worldwide, each with its own classification, location, and direct link. For developers building compliance tools, due diligence pipelines, or corporate intelligence feeds, getting that data into a structured format programmatically is the core challenge. The Minexa

Comcast files regularly with the SEC, and all those filings are listed publicly on cmcsa.com. The page covers everything from 8-K material event reports to proxy statements, Form 4 ownership changes, and Schedule 13G disclosures. Getting that data into a structured format manually means copying row by row. Here is what the extracted output actually looks like, and how to get it in minutes using the Minexa.ai Chrome extension. What the extracted data looks like Each row in the