US state sales tax rates change. Local additions stack on top of base rates. Keeping a clean, current dataset across all fifty states manually is the kind of task that takes hours and goes stale almost immediately. Avalara publishes a structured reference page covering every state, and with the Minexa.ai Chrome extension you can turn that page into a ready-to-export spreadsheet in a few minutes, no code required. Watch the full walkthrough before diving into the steps: What y

Seek is Australia's largest job board. If you are tracking entry-level hiring trends, monitoring which companies are recruiting in regional areas, or building a structured jobs dataset for research or analysis, the listings on Seek contain exactly the data you need. The challenge is that it sits inside job cards on a paginated search results page, not in a downloadable file. This guide shows how to extract that data using Minexa.ai, a web data extraction tool with a Chrome ex

The Companies House SIC code list is a complete reference of Standard Industrial Classification codes used by UK companies when registering or filing. Every active company on the register is assigned at least one SIC code, making this page a foundational lookup table for anyone building company intelligence pipelines, sector filters, or compliance tooling. This guide walks through how to extract the full SIC code dataset from resources.companieshouse.gov.uk/sic/ using the Min

OLX India publishes thousands of active property listings daily, covering rentals, sales, PG accommodations, and commercial spaces across every major Indian city. For a developer building a property market dataset, that volume is exactly the challenge: the data is all there, publicly visible, but extracting it page by page manually is not a realistic option at scale. This walkthrough follows a concrete scenario: setting up a repeatable pipeline that pulls structured property

Collecting clinical trial data by hand means opening dozens of pages, copying fields one by one, and hoping nothing changes between sessions. There is a faster way. The UCSF Clinical Trials browse directory lists active studies across conditions, phases, and study types. It is a structured, publicly accessible source that researchers, analysts, and health data teams regularly need in bulk. The problem is that the site was not built for export. Getting the data out in a usable

The TIOBE Index is one of the most referenced sources for tracking programming language popularity over time. It aggregates search engine query data across dozens of sources and publishes a ranked list of languages monthly. For developers building tooling dashboards, curriculum platforms, or hiring signal pipelines, having programmatic access to that ranking data is more useful than checking the page manually each month. This guide walks through how to extract structured rank

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

Most developers who start building a web scraping pipeline on AWS hit the same wall within the first few hours: the architecture that looks clean on paper turns into a collection of moving parts that each require their own configuration, cost management, and failure handling. This article breaks down the actual decision points, so you can make informed choices before committing to an approach. 1. Public cloud IP ranges get flagged immediately This is the first thing most deve

Trustpilot holds millions of customer reviews across thousands of brands. If you want that data in a structured, queryable format, copying it manually is not a realistic option. This post shows how to build a repeatable extraction pipeline pulling review data from Trustpilot using the Minexa API. The workflow has two phases: train a scraper once using the Minexa Chrome extension, then call the API programmatically to extract reviews at scale across any number of pages. Step 1

If you are building a job market intelligence pipeline for the Canadian market, Simply Hired is a solid data source. The simplyhired.ca Toronto listings page surfaces entry-level roles across dozens of industries, updated regularly. Getting that data into a structured format, reliably and at scale, is what this guide covers. The workflow uses the Minexa API: train a scraper once using the Minexa Chrome extension, then call the API programmatically to extract listings whenever

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

Collecting job listings at scale sounds straightforward until you actually try to do it. The data is right there on the page. Hundreds of fields per listing, thousands of companies, millions of postings updated daily. The problem is not access. The problem is that getting that data into a structured, usable format requires solving a set of technical problems that compound quickly as volume increases. Anyone who has attempted to build a job data pipeline from scratch knows wha

OpenCorporates publishes one of the most comprehensive indexes of company registers available publicly. The registers page at opencorporates.com/registers lists official corporate filing sources from jurisdictions worldwide, each with its own classification, location, and direct link. For developers building compliance tools, due diligence pipelines, or corporate intelligence feeds, getting that data into a structured format programmatically is the core challenge. The Minexa

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

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

Tracking how products are rated across a review platform is straightforward when you are looking at one or two listings. It becomes a different task entirely when you need structured data across dozens of products, updated regularly, in a format you can actually analyse. ProductReview is one of Australia's most visited consumer review platforms. Its category pages list competing products side by side, each with an aggregate rating, a review count, and a preview of the most re

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 developer who has built a scraping pipeline has hit the same wall. It works in the demo. It works the first week. Then something changes and the whole thing falls over quietly, returning empty rows or wrong values with no obvious error. The frustration is not that scraping is hard. It is that it keeps breaking in ways that feel unpredictable. The questions below come directly from the patterns developers run into most often once they move past simple static pages. Why d