The OSPI Online Learning Course Catalog lists every state-approved online course available to Washington K-12 students, including provider names, grade eligibility, course levels, and direct links to provider PDFs. Pulling that data manually means clicking through paginated tables and copying rows one by one. The Minexa API removes that entirely. Watch the full extraction walkthrough before diving into the steps below. What the extracted data looks like Here is a sample of wh

Every job listing on Freshersworld contains more structured data than it appears to hold at first glance. Behind each card sits a salary range, a relative posting timestamp, a numeric job ID embedded in a CSS class, and a full list of eligible qualifications packed into a single traversable array. Getting all of that out cleanly, at scale, without writing selectors or maintaining parsing logic, is exactly what the Minexa API is built for. This guide walks through the complete

Air quality data is publicly available on AQICN, but getting it into a structured format that a pipeline can actually use is a different problem. The page renders pollutant readings dynamically, attribution strings span multiple lines, and each row carries embedded chart images encoded as Base64. None of that is accessible through a simple HTTP request. This guide walks through how to extract that data programmatically using the Minexa API, covering the full workflow from scr

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dozens of pages, copying prices, addresses, and specs one by one, and ending up with a spreadsheet that is already outdated by the time you finish.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.

Clinical trials data is one of the most structurally rich and time-sensitive categories of public research information available on the web. Fred Hutch Cancer Center publishes a continuously updated directory of active trials at fredhutch.org, covering oncology, hematology, behavioral interventions, and surgical comparisons across phase I through phase III studies. Extracting that data programmatically opens up a range of research intelligence workflows that are difficult to

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

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

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

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

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

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

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,

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

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

Preqin publishes structured updates about private markets, fund forecasts, credit ratings, and sustainability classifications. Analysts who track these updates manually visit the page, read through articles, and copy what they need. That works for one article. It breaks down when you need to monitor dozens of updates across weeks or months. The Minexa API solves this by letting you train a scraper once on the Preqin help center articles page, then call it programmatically whe

CSR job listings on IIMJobs change frequently. Roles get posted, filled, and replaced within days. If you are building a talent intelligence pipeline, monitoring hiring trends, or tracking which companies are actively recruiting for CSR functions, manually checking the page is not a practical approach at any real scale. This guide shows how to extract structured jobs data from IIMJobs using the Minexa API, covering the full two-phase workflow: train a scraper once using the M

The Carnegie Classifications directory lists every accredited higher education institution in the United States, each tagged with a classification label, a state, and a student access and earnings designation. That combination of fields makes it one of the more useful structured sources for education research, policy work, and institutional benchmarking. The challenge is that the data lives across paginated HTML pages, not in a downloadable file. This post walks through how t

Every SEC filing J.Jill submits is publicly visible on their investor relations page. The problem is that reading it is easy, but extracting it at scale is not. Form types, filing dates, PDF links, XBRL zips, conversion format arrays, and detail page URLs all sit inside a structured HTML table that changes with every new submission. Manually collecting that data is slow. Parsing it with custom selectors breaks whenever the page updates. This guide shows how to use the Minexa