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

Eventbrite lists thousands of online webinars at any given time. Collecting that data manually, event by event, is slow and does not scale. This guide shows how to extract structured webinar listings from Eventbrite using Minexa.ai, a no-code Chrome extension that turns any page into a structured dataset in minutes. What you get out of the extraction Before walking through the steps, here is a quick checklist of what Minexa pulls from the Eventbrite webinar listing page: Even

The infrastructure question every scraping project eventually hits At some point in every data collection project, the same question comes up: do you build the scraping infrastructure yourself, or do you hand that layer off to something purpose-built for it? On the surface, building your own stack sounds straightforward. Spin up a few containers, add headless Chrome, wire in a proxy pool, handle retries. In practice, each of those components introduces its own maintenance sur

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

Every week, people post requests asking developers to pull data from public websites. The requests are specific: permit volumes by city, trend breakdowns by region, structured output in CSV or spreadsheet format. The person posting knows exactly what they want. They just do not know how to get it without hiring someone. That gap between knowing what data you need and being able to collect it yourself is where a lot of time and money quietly disappears. And in most cases, the

Most developers who integrate the Minexa API use it the same way: train a scraper, pass some URLs, get structured JSON back. That covers the basics. But there is a wider set of capabilities built into the API that rarely gets used, either because it is not obvious from the docs or because the default setup already works well enough that no one goes looking further. This article covers ten of those capabilities, with enough detail to know when each one is worth reaching for. 1

Every piece of data you have ever needed from a website was already sitting there, visible on the screen. The problem was never access. It was format. Web pages are built to be read by humans, not processed by spreadsheets. The information is real, it is current, and it is public. But it lives inside a visual layout designed for a browser, not inside the rows and columns your analysis needs. That gap between what you can see and what you can actually use is where most data co

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

Most data extraction pipelines have the same weak point: the gap between fetching a page and getting usable data out of it. Crawling is solved. Rendering is mostly solved. The part that still costs engineering time is turning raw HTML into a consistent, structured output that downstream systems can actually use. The Minexa API is built specifically for that last step, and it handles more of the pipeline than most developers expect going in. The scraper is the foundation Befor

Every hour spent copying data from a webpage by hand is an hour that produces nothing reusable. The data sits in a spreadsheet, the method lives in no one's head in particular, and the next time the same data is needed, the process starts over from scratch. This is not a niche problem. It shows up across research teams, operations teams, sales teams, and product teams alike. The data they need is publicly visible on the web. Getting it into a usable format is where the time g

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

The Public bond screener sits at public.com/bonds/screener and lists hundreds of corporate bonds with live prices, yields, coupon rates, maturity dates, and credit ratings all on one page. Getting that data into a spreadsheet manually means copying row by row. This walkthrough shows how to extract it in minutes using the Minexa.ai Chrome extension, no code required. What data the Public screener exposes Each row on the screener carries a consistent set of fields. The coupon f

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

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:

LeCEGEP (lecegep.ca) is the central directory for continuing education and professional development courses offered across Quebec's CEGEP network. Hundreds of institutions list their programs there, covering everything from 3D design and database management to workplace safety and HR certification. The data is public and well-structured, but there is no export button and no official API. If you need this data in a usable format, whether to map the continuing education landsca

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