Public records data is technically available to anyone. The problem is that collecting it in a usable format takes far longer than it should. GovTrack publishes detailed legislative information including bills, U.S. Code references, and congressional activity, but that data sits inside web pages rather than downloadable files. Copying it row by row is not a realistic option when you need structured output at any meaningful scale. This guide shows how to extract structured dat

The Federal Reserve publishes foreign exchange rate data on its H.10 release page every week. The data is public, structured in tables, and updated on a fixed schedule. Turning it into a reusable dataset manually means copying rows, reformatting columns, and repeating the process every time the page updates. This guide shows how to extract that data using Minexa.ai, a Chrome extension that detects and exports structured data from any web page without writing code. What the Fe

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

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

Indian equity markets list thousands of companies across BSE and NSE, and finding a clean, structured version of that company directory is harder than it should be. Tradebulls maintains a browsable company listing at tradebulls.in/company-list/n that covers company names, BSE codes, NSE ticker symbols, and direct profile links. The data is there, but it is paginated across multiple pages and not downloadable out of the box. This guide shows how to extract that data into a cle

The Michelin Guide has been a trusted reference for travelers for over a century. Today, its website lists hotels, stays, and travel destinations with rich structured detail: names, locations, categories, ratings, and more. That data is publicly visible, but pulling it into a usable format page by page is slow and error-prone work. This guide shows how to extract that data using Minexa.ai, a web data extraction tool that works directly from your browser. No code, no selectors

The UK government's Primary Authority register at primary-authority.beis.gov.uk/par holds a publicly accessible directory of franchise and business opportunity partnerships. Getting that data into a spreadsheet without copying row by row is exactly what this guide covers. Install the Minexa.ai Chrome extension and follow the steps below to turn the register into a clean, structured dataset in a few minutes. What data is on the page The Primary Authority register lists partner

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

Forum threads hold more structured data than they appear to. Every comment has an author, a timestamp, a unique ID, and sometimes edit history. If you need to collect that data at scale, copying it manually is not realistic. This guide shows how to extract it cleanly from Zotero Forums using the Minexa.ai Chrome extension. What data is available on a Zotero Forums thread The target page is a discussion thread on forums.zotero.org. Each comment in the thread exposes a consiste

The NC.gov grant opportunities page lists every active state grant program in one place, organized by category. Getting that data into a spreadsheet manually means copying row by row across dozens of programs. With the Minexa.ai Chrome extension, the whole page becomes a structured dataset in a few minutes. Here is exactly what the process looks like, step by step. What data you get Each row on the NC.gov grant opportunities page contains four fields Minexa extracts cleanly:

Most developers get a Minexa.ai API pipeline running quickly. The default request body works for a large share of sites, and the Chrome extension generates ready-to-use Python code in minutes. But a handful of parameters sit quietly in the request schema and go unused, even when they would directly solve a problem the developer is already fighting with. Here are ten parameters worth knowing before you hit your first wall. 1. bypass: anti-bot handling that only activates on se

Wise is best known as a money transfer platform, but it also hosts a publicly accessible stock directory covering thousands of listed companies across global exchanges. Each category page on wise.com lists company names, ticker symbols, and links to individual stock detail pages. If you need that data in a structured format for research, financial analysis, or portfolio tooling, copying it manually is not realistic at any meaningful scale. This guide shows how to extract that

VesselFinder tracks hundreds of thousands of ships worldwide. The vessel list at vesselfinder.com/vessels is one of the most complete public directories of maritime assets available, covering cargo ships, tankers, cruise liners, and more. Getting that data into a spreadsheet for analysis, research, or fleet monitoring normally means a lot of manual copying. This tutorial shows how to do it automatically using the Minexa Chrome extension. What data you can extract from VesselF

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