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

Uber Eats city pages list every restaurant available for delivery in a given area, complete with ratings, cuisine types, delivery times, and promotional labels. That data is publicly visible but entirely unstructured. Pulling it manually is slow and does not scale. This guide shows how to extract it programmatically using the Minexa API. What the data looks like Before walking through the extraction steps, here is a sample of the structured output Minexa returns from an Uber

WorkBC publishes one of the most complete public job boards in British Columbia, covering roles across every sector, employment type, and region. For developers building labour market tools, recruitment pipelines, or regional salary datasets, that listing page is a structured data source waiting to be tapped. This guide shows how to extract that data at scale using the Minexa API. Train a scraper once via the Chrome extension, then call the API programmatically against any vo

Finanzfluss is one of Germany's most widely used personal finance platforms. Its ETF search tool at finanzfluss.de/informer/etf/suche/ lists hundreds of funds with structured attributes including total expense ratios, fund volumes, share class sizes, launch dates, distribution policies, and replication methods. For developers building investment research pipelines, fund comparison tools, or cost-monitoring workflows, this page is a reliable and regularly updated data source.

Zapier's app directory is one of the most complete public indexes of SaaS and software tools available. Each category page lists dozens of products with structured metadata: tool names, descriptions, logo assets, and direct integration links. For anyone building a SaaS intelligence pipeline, competitive landscape tracker, or integration catalog, that data is genuinely useful. The challenge is getting it out in a structured, repeatable way. This guide walks through how to do e

The USDA Rural Development lender directory at rd.usda.gov/resources/lenders lists hundreds of approved mortgage lenders across the United States, each with a company name and a direct website link. For developers building fintech tools, mortgage research pipelines, or lender qualification workflows, that data is genuinely useful. The problem is that it sits in an HTML table with no public API and no bulk export option. This guide walks through how to extract that lender data

Collecting job market data from Jora manually is slow, inconsistent, and breaks the moment you need more than a handful of listings. The Minexa API gives developers a repeatable, structured extraction pipeline that works across thousands of pages without writing a single selector. This guide covers the full workflow: training a scraper on Jora, retrieving your scraper_id, and calling the API to extract job listings at scale. What data Jora exposes Each listing on a Jora searc