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Guides to scraping, datasets and pricing data

Practical articles written from daily work with extractors in production: when to build and when to buy data, how to normalize manufacturer prices and how to set up price tracking you can trust.

These guides answer the questions that come up most when a company starts working with web data: whether it should build its own scraping, how to compare prices that come with different tax bases and how to keep competitor tracking that someone actually reads. Each one starts from real examples, with fields and rows from our samples, and avoids generic figures that cannot be verified.

If you would rather see the data before reading, the downloadable samples contain real rows in JSON and CSV, up to 100 records per source, already sanitized of personal data. And if you already know what you need, the dataset catalogue and the managed web scraping service are the next step.

Build vs buy web scraping: how to decide
Writing a scraper is easy; maintaining it, validating it and delivering reliable data every week is what takes the effort. This guide walks through the real work behind each option and a simple way to choose.
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Normalizing car configurator pricing: list, net and discount
Each manufacturer answers "how much does this car cost?" with different fields. This guide shows how to read real configurator fields and map them to a common schema without mixing tax bases.
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How to monitor competitor prices with web data
Good price tracking is not a scraper that runs every night, but a process with a defined scope, matched products, comparable prices and alerts that someone reads. These are the steps.
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