Inside Event Data: How Global Listings Are Collected, Cleaned and Ranked
Type a city name into an event search box and you get a clean grid of concerts, matches and screenings, each with a date, a venue and a ticket link. It looks effortless. Behind that grid sits one of the messiest data problems in consumer software — a problem that has to be re-solved every single day, in every time zone, forever.
Event data does not arrive tidy. It arrives as HTML from a venue built in 2011, as a promoter's spreadsheet, as a ticketing API with its own idea of what a "city" is, and as a social post announcing a date that will later change. Turning that into something a person can browse is a pipeline problem, and it is worth understanding — both if you build these systems and if you simply want to know why your search results look the way they do.
Stage 1: Ingestion, and the myth of the clean feed
The first job is collecting listings from wherever they live. Large ticketing platforms expose APIs, which is the easy half. The hard half is everything else: independent venues, festival organisers, cinemas, university societies, sports clubs, esports leagues and community groups, most of whom publish to a website and nothing more.
A serious index therefore runs several ingestion modes at once — API pulls on a schedule, structured feed parsing where a site exposes one, and careful extraction where it does not. Each source has its own refresh cadence, its own reliability profile, and its own habit of silently changing format on a Tuesday afternoon without warning anyone.
The duplicate problem, illustrated
One festival appears in the index four times: once from the ticketing API as "Ejekt Festival 2026", once from the venue as "EJEKT FEST — Day 1", once from an aggregator as "Ejekt Festival (Athens)", and once from the promoter with a different start time because they listed doors rather than first act. All four are the same event. A user who sees four cards loses trust in the entire product.
Stage 2: Entity resolution — the real engineering
Deduplication is where event platforms are quietly won and lost. The system must decide whether two records describe the same event, and it must decide correctly at scale, without a human reading each pair.
The usual approach combines fuzzy string similarity on the title, normalisation of the artist or performer name, proximity of the venue coordinates, and closeness of the start time within a tolerance window. Any one signal is unreliable on its own. A tribute act shares a name with the real band. Two different clubs share an address in a shopping complex. Titles differ in punctuation, capitalisation, diacritics and the eternal question of whether "&" and "and" are the same character.
Stage 3: Places, not strings
Venues and cities are the second hard problem. A listing that says "The Warehouse, Manchester" must resolve to a specific building with coordinates, because users search geographically. Free-text location strings are useless for the query "what is happening near me this weekend".
Geocoding, venue canonicalisation and city-level roll-ups therefore run as their own pipeline stage, and they carry a permanent maintenance burden: venues close, rename, rebrand under sponsorship deals, and occasionally reopen under a new name in the same room. Platforms that index worldwide — services such as StungEvents, which cover concerts, festivals, sports, cinema and esports across hundreds of cities — face this at a scale where manual curation stops being an option and the resolution logic has to carry the weight alone.
Stage 4: Ranking what the user actually sees
Once the data is clean, something must decide the order. Naïve chronological sorting produces a wall of undifferentiated listings that helps nobody. Useful ranking blends several signals:
- Proximity to the user's detected or chosen location
- Date relevance — tonight and this weekend outrank a show in nine months
- Popularity and demand signals, including how many users have expressed interest
- Category balance, so one busy vertical does not swallow the entire page
- Data completeness, since a listing with no venue or image converts poorly and reads as spam
The freshness problem nobody talks about
Events are unusual among data objects: they expire, and they change up to the moment they expire. Support acts are added, start times shift, venues are swapped after a licensing dispute, and shows are cancelled outright. A pipeline that ingests correctly but never revisits its records will be confidently wrong within a fortnight.
The standard answer is tiered refresh. Events happening within seven days are re-checked aggressively; events a month out are checked periodically; events beyond six months are checked rarely, because their details are provisional anyway. Marking an event as cancelled — rather than deleting it silently — matters too, since a user who bought tickets deserves to see why the listing changed.
Why an open API changes the ecosystem
The final stage of maturity for an event index is exposing it. Once listings are clean, resolved and ranked, they become infrastructure that other products can build on: local news sites publishing "what's on today" pages, city guides, voice assistants answering questions about tonight, travel apps suggesting what is happening at the destination. Each integration widens coverage in the other direction too, because downstream users report errors the pipeline never caught.
The takeaway
The visible product — a search box and a grid of cards — is roughly five percent of an event platform. The rest is ingestion, entity resolution, geocoding, freshness management and ranking, running continuously against sources that were never designed to be machine-readable. When it works, nobody notices. That invisibility is precisely the point, and it is the most honest measure of whether the pipeline underneath is any good.