YouTube Scraping

What's included

We collect public YouTube data and deliver it as a ready-made body of data for market and competitor analysis. The work includes gathering channels and their statistics, video lists with views, likes, and dates, metadata — titles, descriptions, tags — as well as comments under videos. We tailor collection to the task: monitoring competitors' growth rates, breaking down their content strategy, finding popular topics and formats in your niche. We export the result to a table, a database, or your analytics, and put collection on a schedule to track dynamics. We work with publicly available data and respect the platform's restrictions. What you see is what competitors publish and how, and what actually racks up views — instead of manually watching dozens of channels.

How it really works

Collection is built on cloud actors that access YouTube's public pages and API data and store the result in a structured dataset. We set the input — a list of channels, keywords, or video links — and the depth parameters: how many videos and comments to pull. The actor works through the lists with paginated loading and returns a table where each row is a video or channel with a set of metrics: views, likes, date, duration, tags. Next the data is normalized — dates and numbers are standardized, duplicates removed, and derived indicators calculated, such as the rate at which views accumulate. The finished body of data is exported in the required format or into your system, and repeated collection on a schedule shows dynamics over time rather than a single-day snapshot.

Where video hosting and data collection came from

YouTube was founded on February 14, 2005 by Chad Hurley, Steve Chen, and Jawed Karim, and the first video in history, "Me at the zoo," was uploaded by Karim on April 23, 2005; in November 2006 the service was bought by Google for 1.65 billion dollars. Automatic data collection from the web is older than YouTube itself: the first web robot was the World Wide Web Wanderer, deployed by Matthew Gray at the Massachusetts Institute of Technology in June 1993 to measure the size of the web. It was precisely from the idea of automatically crawling pages that both search engines and modern social-media analytics grew. The Apify cloud collection platform we work on appeared in 2015 and made scraping a managed process with ready-made tools. We use this mature stack, rather than fragile homegrown parsers.

Why accuracy and legality are critical

Data about competitors is useful only if it can be trusted. A collection error distorts the picture: missed videos understate a channel's activity, mixed-up metrics lead to wrong conclusions about what works in the niche. That's why the key work is not the visit to the page itself, but normalization: correct dates, honest metrics, no duplicates, and correctly calculated dynamics. The legal side matters just as much: we collect publicly available data, respect rate limits, and don't bypass protections, which separates competitive analytics from a violation. We say plainly which data is publicly available and which isn't, and we don't promise the impossible. As a result, you get a reliable picture of the niche's content market, rather than a set of random numbers you can't rely on.

What stack we work with

The foundation is the Apify platform's cloud actors for YouTube: they collect channels, videos, metadata, and comments, run in the cloud, and don't depend on your hardware. We build orchestration and scheduling on n8n, tying collection, cleaning, and export into a single automated flow. We store the result in a table, a database, or object storage, and where needed feed it into your CRM or analytics dashboard. For competitor monitoring, we set up a regular run to track view dynamics and publishing frequency over time. The stack is manageable and portable: the collection logic is described explicitly and stays with you, it can be extended for new tasks without depending on a single homegrown script.

When the key tools appeared

Video-data collection tools took shape through three milestones. The web robot World Wide Web Wanderer appeared in June 1993 and set the idea of automatic crawling. YouTube as a data source launched on February 14, 2005 and over two decades became the largest video hosting service. The Apify cloud scraping platform was founded in 2015 by Jan Čurň and Jakub Balada and turned scattered parsers into a managed platform. The n8n orchestrator was released as an open-source project in 2019 and made it possible to link collection and processing without manual code. We use the current generation of these tools, so collection is resilient and scales, rather than breaking at the first platform change.

Why you can trust us with this

Our team's combined IT experience exceeds 45 years, and we run analysis of video platforms systematically, not as a one-off. We fix the goal, configure the actors, always clean and normalize the data, and check its completeness before handover. We keep the legal framework honest: public data, respect for the platform's restrictions, no promises of access to what's private. Both the data and the configured pipeline stay yours — you can repeat the collection without us. We'll tell you plainly where competitor monitoring is useful and where it's excessive. As a result, you get a reliable picture of competitors' content strategy and niche trends, rather than a pile of raw rows with no conclusions.