A club night listed for Friday opens at one in the morning on Saturday. Store the calendar date and every time-based query you build is off by a day.
ScrapeIt runs the collection as a managed service. You name the cities, venues or genres; we build the pipeline with lineups as rows, resolve start timestamps alongside the advertised date, keep ticket tiers separate, and hand back CSV, JSON, Excel or a push into your warehouse.
Where price steps or sell-outs matter we run repeat collection and keep every observation, since a tier that closed leaves no trace of having existed.
We collect published listing data only, honour the crawl rules the site publishes and pace requests. No attendee data, no account access. Terms restrict commercial reuse, so the dataset is for analysis rather than republication, and your counsel should see the use case before the project starts.
The event record covers the event name, venue, city, country, the advertised date, a resolved start timestamp, the end time where published, music genres and the promoter or event series where stated.
The advertised date and the resolved start are both kept. The first is what the platform displays and what a user would recognise; the second is what any time-based analysis needs. Keeping only one of them guarantees an argument later about why the numbers disagree with the site.
Lineup is delivered as rows: event, artist, and billing position where it can be determined. That turns a listing into a graph in which artists, venues and cities connect, which is what makes the source interesting beyond a calendar.
Ticket tiers are their own rows with the tier name, price and currency, since early release, advance and door prices are different products sold at different times.
Every row carries the collection timestamp, which for a tiered ticket is what makes the price meaningful at all.
The artist graph is the distinctive output. Once lineups are rows, the questions are network questions: which artists play together, which venues book which scenes, how a touring artist moves between cities, and which venues are gaining or losing bookings. None of it is answerable from a listings table.
Venue programming analysis follows from the same data and is what venue operators and promoters actually buy: what a competitor is booking, how often, and at what price point.
Price step tracking needs repeat collection, because the interesting fact is when a tier sold out rather than what it cost. That is a change record and it starts when collection starts.
What stays out of scope is attendees. No account data, no ticket holders, no social connections. Artist names as billed on a public lineup are professional credits and are collected as such; anything about the people buying tickets is not collected at all.
Xceed is a nightlife and electronic music platform covering clubs and parties across Spain, Italy, Portugal and other European markets. Its listings are club nights: a venue, a date, a lineup of DJs, and ticket tiers that usually change as the night approaches.
Two things make this different from general event data. The first is that the lineup is the product. People choose a night by who is playing, and an event with six artists is six relationships, not a text field. Stored as a string, the lineup cannot answer a single question about which artists play where, how often, or alongside whom.
The second is time. Club events routinely start after midnight, so a night advertised as Friday physically happens in the early hours of Saturday. Storing the advertised calendar date without resolving it to an actual start timestamp puts every event in the wrong day bucket, and the error is invisible until somebody builds a weekday analysis on it.
City pages respond directly with substantial content, and crawl rules are published and unusually permissive, disallowing only the password reset and authentication paths.
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Nightlife data looks like ordinary event data and behaves differently in two specific ways, and both of them are decided at collection time rather than fixable afterwards.
The lineup is the first. An artist booking dataset - who plays which venues, in which cities, how often, and with whom - is the reason most clients come to this kind of source, and it exists only if artists were extracted as separate rows linked to events. Recovered later from a text field, artist names are ambiguous, collapsed with b2b billings and missing the ones written differently on different nights.
The timestamp is the second. An event starting at one in the morning belongs to the night before in how people talk about it and to the following day in how a computer sorts it. Both representations are needed and only one of them is printed on the page, so the other has to be derived while the context is still there.
The third reason is ticket tiers. Nightlife pricing moves in steps - early release sells out, advance opens, door price applies - and each step is a different price for the same night. A single price column records whichever step happened to be live when the crawler ran.
The fourth is that this is a regional source, strong in southern Europe and thin elsewhere. It is an excellent window onto Spanish, Italian and Portuguese nightlife and a poor one onto anywhere else, and we scope it that way.
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, maintains artist name resolution as billings change, and repairs the collector before a booking history loses a season.
You see a sample first, in your format, over the cities and venues you actually track, with lineups expanded and timestamps resolved so you can judge the hard parts on real nights.
Because a night advertised as Friday often starts at one in the morning on Saturday. The advertised date is what the site shows and what a user recognises; the resolved timestamp is what any time-based query needs. Keep only one and the numbers will eventually disagree with the site.
Yes, and it is the main reason to use this source. Lineups come as rows linking artists to events, which supports questions about who plays where, how often and alongside whom. Recovered later from a text field those names are ambiguous and incomplete.
Each tier is its own row with a timestamp. Early release, advance and door are different products sold at different moments, and a single price column just records whichever one was live when the crawler ran.
No. It is strong in Spain, Italy and Portugal and thin elsewhere. That makes it an excellent source for southern European nightlife and a poor one for a global picture, and we scope briefs accordingly rather than letting coverage be assumed.
No. No account data, no ticket holders, no social connections. Artist names on a public lineup are professional credits and are collected as such; nothing about the people buying tickets is collected at all.
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
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