Tracking used car prices across a city like Mumbai page by page is the kind of task that sounds manageable until you actually try it. CarTrade lists hundreds of second-hand vehicles with prices, mileage, fuel type, and photo galleries per listing. Getting all of that into a spreadsheet manually takes hours. This guide shows how to do it in minutes using the Minexa.ai Chrome extension. Watch the full tutorial first The video below walks through the entire extraction from openi

Tracking every press release, product announcement, and leadership update from a fast-moving digital bank like Starling Bank by hand is the kind of task that sounds manageable until you actually try it at scale. Starling Bank's news page at starlingbank.com/news/ publishes a steady stream of articles covering everything from AI feature launches and bond issuances to SME research and leadership appointments. If you need that data in a structured format for competitive monitori

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

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

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

Manually copying flight data from Cheapflights row by row is the kind of task that looks simple until you are three pages in and the prices have already changed. This guide shows how to connect the Minexa API to Cheapflights and pull structured flight data programmatically, starting from a single trained scraper. What the data looks like before and after Without automation, flight data from Cheapflights lives in a rendered page that updates dynamically. You can see the prices

BSE India publishes live derivatives market data on its streamer page, and that data updates constantly. If you want to capture it, analyze it, or track it over time, copying rows manually is not a realistic option. This guide shows exactly how to extract it using the Minexa.ai Chrome extension, step by step, with no code required. The target page is the BSE India equity streamer market watch at bseindia.com/eqstreamer/streamermarketwatch?flag=2. It lists the most active SENS

Supply chain data is everywhere on the web, but getting it into a spreadsheet without copying it line by line is a different story. This walkthrough shows how to pull structured article data from SupplyChainBrain, one of the most referenced logistics and supply chain publications, using the Minexa.ai Chrome extension. The starting URL for this tutorial is the logistics service providers directory: https://www.supplychainbrain.com/directories/98-supplier-directory/topic/1254-l

Michael Page publishes hundreds of active job listings across London every day. If you want that data in a spreadsheet, structured and ready to analyse, the Minexa.ai Chrome extension gets you there without writing a single line of code. This walkthrough covers the full extraction process from the michaelpage.co.uk/jobs/london page, showing exactly what you see at each step and what to do. Watch the full tutorial first The video below covers the complete workflow from install

Government procurement portals publish some of the most consistently structured public data available online. OregonBuys, Oregon's centralized public purchasing system at oregonbuys.gov, lists open bids from state agencies, counties, school districts, tribal organizations, and public utilities all in one place. The data is public, updated regularly, and genuinely useful for vendors tracking opportunities, researchers monitoring public spending, and analysts building procureme

FRED publishes hundreds of commodity price series, all publicly accessible, all updated regularly. The problem is that the data sits inside a category listing page that was built for browsing, not for export. Getting it into a spreadsheet means either copying rows one by one or building something custom. Neither option scales. This guide shows how to extract structured commodity price data from fred.stlouisfed.org using the Minexa.ai Chrome extension, no code required. Watch

Flight route data from Kayak is genuinely useful for travel research, price monitoring, and competitive analysis. This guide walks through exactly how to extract it using the Minexa.ai Chrome extension, no code required. Watch the full tutorial first Before going through the steps below, watch the video walkthrough. It covers the entire extraction from start to export in a single session. Checkpoint 1: open Minexa.ai and navigate to Kayak Install the Minexa.ai Chrome extensio

OpenEI hosts one of the most comprehensive collections of publicly available energy and environmental datasets. The data is there, clearly listed, but getting it into a spreadsheet or a structured file requires clicking through pages and copying entries one by one. This walkthrough shows how to extract that data automatically using the Minexa.ai Chrome extension, with no code required. Watch the extraction in action Before going through the steps, watch the full tutorial belo

Most people who try web scraping for the first time give up before they collect a single useful row of data. Not because the data is not there. It is right there on the screen. The problem is everything that sits between the visible page and a clean, usable spreadsheet. This post is about that gap, what causes it, and what it actually takes to close it. The problem looks smaller than it is When someone decides to start collecting web data without a technical background, the f

Naukri lists thousands of H1 visa sponsorship roles across India, covering everything from immigration consulting to senior engineering positions. The data is public and updated frequently, but collecting it in a structured form manually is slow and hard to repeat. This walkthrough shows how to extract that data using the Minexa.ai Chrome extension, no code required. Watch the tutorial first The video below covers the full extraction from start to export. Watch it once before

Comcast files regularly with the SEC, and all those filings are listed publicly on cmcsa.com. The page covers everything from 8-K material event reports to proxy statements, Form 4 ownership changes, and Schedule 13G disclosures. Getting that data into a structured format manually means copying row by row. Here is what the extracted output actually looks like, and how to get it in minutes using the Minexa.ai Chrome extension. What the extracted data looks like Each row in the

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

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

You open a website. The data you need is right there, laid out in rows, clearly labeled, exactly what your project requires. Then you realize you have to get it out of the page and into a spreadsheet, and that is where things stop being simple. Copying row by row is not realistic if there are hundreds of results. Building a scraper requires knowing how the site is structured in code. Hiring someone to do it takes time and budget you may not have. And if the site updates its l

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