There is no catalogue to browse here, there is a schedule that already started. Miss the hour and that slot is gone with no record of it.
ScrapeIt runs the collection as a managed service. You name the regions and channel categories; we build the pipeline capturing schedules as they run, with timezone and region on every row and the on demand section typed separately, and hand back CSV, JSON, Excel or a push into your warehouse.
Collection has to run continuously to be worth anything here, so this is a monitored feed rather than a one-time job, and we quote it as one.
We collect published schedule metadata only - never streams, never anything behind a sign in, no viewer data - honour the crawl rules and pace requests. Content is copyrighted and terms restrict reuse, so the dataset is for analysis rather than republication. Your counsel should see the use case before the project starts.
Channel records carry the channel name, number, category or genre, language, region and the description as published.
Schedule records are keyed to the channel: programme title, series and episode identifiers where published, start time with timezone, end time, duration, genre and rating. Timezone is not optional on a service scheduling across regions - a slot time without it is a guess.
Region is a first class field because channel line-ups differ between countries. The same service name carries different channels in different markets, and merging them produces a line-up that exists nowhere.
The on demand section is typed separately, since a title in a library and a slot in a schedule share almost no fields and the distinction is the whole point of this source.
Every row carries the collection timestamp, which on schedule data is what tells you when the observation was actually made rather than when the slot was supposed to run.
Repetition analysis is the distinctive output: how many times a title runs across a week, on how many channels, and at what times. For a rights holder it is a direct measure of how their content is being used; for an advertiser it describes the inventory.
Daypart analysis - what fills mornings versus prime time - shows how a free service programmes around audience patterns, which is a question broadcast analysts have asked for decades and rarely get to ask of streaming.
Channel launch and closure tracking is a cheap by-product of collecting the line-up regularly, and it is a readable record of what the operator thinks is working.
Limits: metadata only, never streams, nothing behind a sign in, no viewer data. And history begins when collection does, which on this source is the single most important thing to agree before quoting.
Pluto TV is a free ad-supported streaming service built around linear channels. Rather than a library a viewer browses, it runs hundreds of themed channels on schedules, much like broadcast television, alongside a smaller on demand section.
That makes it structurally different from every subscription service in this section and it changes the data problem completely. A catalogue is a list that persists; a schedule is a sequence of slots that happen and then stop existing.
The consequence is that the dataset has to be captured as it runs. What was on channel forty-two at nine in the evening last Tuesday is not published afterwards, so the schedule is a time series by construction and only exists from the moment collection begins.
Channel and live schedule pages respond directly with a large amount of content. Crawl rules are published and disallow the account area and parameterised addresses, leaving the schedule surfaces open.
Get a Quote
Developers
Customers worldwide
Pages extracted
Hours saved for our clients
€199 / one-time
setup fee - included
€169 / mo
setup fee €499
€229 / mo
setup fee €499
€349 / mo
setup fee €499
€549 / mo
setup fee €799
Clients arrive asking for the Pluto TV catalogue and there is not really one, which is worth saying in the first minute rather than delivering something adjacent.
What there is instead is more interesting for the questions people usually have. Free ad-supported linear television is a growing category and almost nobody has structured data about what actually runs on it. Which content fills the hours, how often a title repeats, how much of a channel is licensed library versus originals - those are schedule questions and they are answerable only from observation.
The second point is that the schedule is perishable in a way catalogues are not. A title removed from a subscription library at least leaves the library behind; a slot that has run leaves nothing at all. There is no back catalogue to buy, and a client wanting a year of schedule history needs a year of collection.
The third is repetition, which is the defining economics of this model. Free linear channels repeat heavily, and measuring how heavily - per channel, per title, per daypart - is the analysis rights holders and advertisers actually want. It falls straight out of a properly collected schedule and is invisible from anything else.
The fourth is regional line-up differences, which are substantial and only visible if region is collected as a field from the start.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, watches it as channel line-ups change, and repairs it quickly, because on schedule data an outage is a permanent hole rather than a delay.
You see a sample first, in your format, over the regions and channels you actually care about, with a real observation window so you can see repetition in the data rather than take it on description.
There is not really one, and that is worth saying in the first minute. It is a linear service: hundreds of channels on schedules. The on demand section is smaller and typed separately. The schedule is the dataset.
No. A slot that has run leaves nothing behind - unlike a library title, which at least leaves the library. There is no back catalogue to buy, so a year of schedule history needs a year of collection.
For a rights holder it measures how their content is actually being used - how many runs, on how many channels, in which dayparts. For an advertiser it describes the inventory. It falls straight out of a properly collected schedule and is invisible from anything else.
Because channel line-ups differ substantially between countries. The same service name carries different channels in different markets, and merging them produces a line-up that exists nowhere. Region is a field from the first row.
That day is permanently missing, which is why we treat outages here more seriously than on catalogue sources. On a schedule feed an interruption is a hole in the record rather than a delay in delivery.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
