Government form catalogues hold more structured data than they appear to at first glance. The Service Canada eForms catalogue lists hundreds of official government forms across departments including Employment and Social Development Canada, Human Resources and Social Development Canada, and others. Each entry carries a form code, a descriptive title, an issuing department, and a link to the form detail page. Collecting that data manually, row by row, across multiple pages is

Foundit is one of Southeast Asia's most active job platforms, and its Philippine portal at foundit.com.ph lists thousands of accounting, finance, and professional roles updated daily. If you are building a hiring intelligence tool, a salary benchmarking dataset, or a job market tracker, that volume of structured listing data is exactly what you need. The challenge is getting it out in a usable format without writing and maintaining a custom scraper from scratch. This guide sh

Restaurant data is genuinely useful for a wide range of applications: hospitality intelligence platforms, travel recommendation engines, food media products, and market research tools all depend on structured, reliable listings. AGFG (agfg.com.au) is one of Australia's most established dining guides, covering awarded restaurants across every state with chef hat ratings, cuisine classifications, and regional breakdowns. The challenge is that none of this data is available via

Collecting bike specification and pricing data from BikeDekho manually means opening dozens of pages, copying values one by one, and still ending up with a spreadsheet that goes stale the moment prices update. This walkthrough shows how to extract that data automatically using the Minexa.ai Chrome extension, no code required. Watch the full video tutorial first, then follow the steps below. Step 1: Open Minexa.ai and navigate to BikeDekho Install the Minexa.ai Chrome extensio

Trading Economics publishes a live stream of commodity prices, currency rates, and market indicators across hundreds of instruments. The data is public, structured, and updates continuously. Getting it into a pipeline programmatically is a different story. This guide walks through how to extract structured alternative data from tradingeconomics.com/stream using the Minexa API. The workflow has two phases: train a scraper once using the Minexa Chrome extension, then call the A

Collecting job listings from Adzuna manually means opening pages, copying rows, and repeating that across dozens of search result pages. The Minexa API replaces that entire process with a single trained scraper and a POST request. This guide walks through how to extract structured job data from adzuna.com.au using the Minexa API, covering the full workflow from training the scraper to calling the API and working with the fields it returns. Before and after: navigating to Adzu

Legal and compliance data scattered across dozens of pages is genuinely difficult to work with. LegalDesk publishes a wide catalogue of contract templates, agreement guides, and compliance documents, and getting that content into a structured format manually takes far longer than it should. This walkthrough shows how to extract that data using the Minexa.ai Chrome extension in a few minutes, no code required. Watch the full tutorial first Outcome: a confirmed starting point o

Getting data from a website into Excel sounds simple until you actually try it. The page loads fine in your browser, the data is right there, but copying it manually takes hours, and the built-in Excel tools stop working the moment a site uses JavaScript or blocks automated requests. Here is a clear breakdown of every practical method, what each one handles well, and where each one falls short. Method 1: Excel Power Query (built-in) Excel has a built-in feature under the Data

Automation pipelines have quietly become some of the most complex systems small teams maintain. What started as simple task handoffs between apps has turned into branching logic, conditional retries, multi-step API orchestration, and AI-assisted decision layers. The tools have kept up. The data feeding those pipelines often has not. This is where most workflows quietly break. Not at the orchestration layer, but earlier, at the point where structured, reliable data needs to en

Indonesia's logistics sector spans hundreds of verified freight forwarders, cargo agents, and supply chain operators, all listed publicly on Indonetwork (indonetwork.co.id). Getting that data into a spreadsheet manually is slow and error-prone. This guide shows how to extract it in minutes using the Minexa.ai Chrome extension, no code required. Stage 1: Open the directory and launch the extension Navigate to the Indonetwork cargo and logistics company directory at indonetwork

Car listing pages are packed with structured data. Every search result contains make, model, trim, price, mileage, location, dealer name, fuel type, transmission, and more. But that data sits locked inside HTML, visible on screen and completely inaccessible at scale without a scraping workflow. For automotive marketplaces, that gap is a real operational problem. Manually collecting listing data across dozens of sources is slow, inconsistent, and impossible to maintain. This p

Building a portfolio dashboard in Google Sheets sounds like a weekend project. Then you realize that the built-in spreadsheet functions only go so far, the data you actually need is not available through any official API, and every workaround you try either breaks silently or stops working after a few days. That gap between what you can see on a financial website and what you can reliably use in a spreadsheet is where most self-built dashboards stall. This post is about why t

IMDb holds some of the most detailed publicly available entertainment data on the internet. Episode lists, air dates, ratings, cast details, and season breakdowns are all sitting right there on the page. The problem is that none of it is structured in a way you can actually work with. If you have ever tried to copy episode data from IMDb into a spreadsheet manually, you already know how quickly that falls apart. This post walks through how to extract that data cleanly using t

Commercial property data sitting inside a browser tab is not useful to a pipeline. Structured rows in a dataset are. This guide shows how to extract commercial real estate listings from OnTheMarket using the Minexa API, covering every step from scraper training to a working Python request. Watch the full walkthrough first The video below covers the complete extraction workflow on OnTheMarket's commercial listings page for Kent. Watch it before going through the screenshots so

Flight price data sitting behind a search interface is genuinely difficult to collect at scale. Kayak surfaces destination tiles with prices, stop counts, and editorial tags, but none of that is available via an official API. This walkthrough shows how to extract it using the Minexa API, a data extraction platform that combines a browser-based training step with a programmatic extraction endpoint. The workflow has two phases: train a scraper once using the Minexa Chrome exten

Your scraping bill started small. Then it doubled. Then it doubled again. And now it is sitting at a number that makes the whole operation feel fragile. This is not an unusual trajectory. Developers building data-dependent products often hit the same pattern: a scraping setup that works fine at low volume becomes increasingly expensive and increasingly unreliable as the product grows. The costs scale faster than the revenue. The service goes down at the worst moments. And the

The London Metal Exchange publishes a steady stream of market reports, press releases, member notices, and metal price pages. If you are tracking copper warrant stocks, monitoring off-warrant reporting changes, or building a dataset of LME announcements over time, the search interface at lme.com/search?searchTerm=stocks surfaces exactly the kind of structured content you need. The problem is that it is all locked inside a web page, not a spreadsheet. This guide walks through

Financial product pages like Kotak Mahindra Bank's credit card listings are structured, publicly accessible, and updated regularly. That makes them a practical data source for fintech developers, competitive analysts, and financial researchers who need card features, eligibility criteria, and product metadata in a structured format. This guide covers how to extract that data programmatically using the Minexa API, a deterministic web extraction platform that handles JavaScript

Event data sitting behind a paginated listing page is useful only when it is structured, repeatable, and accessible without manual copying. Live Nation Asia publishes a full event directory at livenation.asia/event/allevents, and the Minexa API gives developers a direct path from that page to a clean JSON dataset they can query, store, or pipe into any downstream system. This guide walks through the complete workflow: training a scraper once using the Minexa Chrome extension,

If you track Indian startup funding rounds, monitor sector trends, or build datasets for VC research, Inc42 is one of the most consistently updated sources available. The challenge is that the data sits inside article cards spread across paginated listing pages, and there is no official API to pull it programmatically. This guide shows how to extract structured startup and investment data from Inc42 using the Minexa API. The workflow has two phases: train a scraper once using