We extract event schedules, ticket tiers, waitlist dynamics, venue coordinates, and artist graphs from Dice.fm. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Event Listings objects from dice.fm. All fields typed and schema-versioned.
"event_id": "64a7b29f0c", "title": "Bicep Live at Printworks", "date": "2026-11-14", "time": "22:00", "status": "sold_out", "age_limit": "18+", "genre_tags": "['Electronic', 'Techno', 'House']"
| # | event_id | title | date | time | timezone | status |
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Complete list of extractable fields for Ticket & Pricing objects from dice.fm. All fields typed and schema-versioned.
"event_id": "64a7b29f0c", "ticket_type": "Final Release", "price": 45.5, "currency": "GBP", "is_sold_out": true, "waitlist_active": true, "face_value": 40.0, "booking_fee": 5.5
| # | event_id | ticket_type | price | currency | availability | is_sold_out |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Venue Data objects from dice.fm. All fields typed and schema-versioned.
"venue_id": "v-9921", "name": "Printworks London", "city": "London", "country": "UK", "latitude": 51.4975, "longitude": -0.0435, "capacity": 6000, "accessibility_info": "Wheelchair accessible, disabled toilets available"
| # | venue_id | name | address | city | country | latitude |
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Complete list of extractable fields for Artist & Lineup objects from dice.fm. All fields typed and schema-versioned.
"artist_id": "a-8823", "name": "Bicep", "role": "Headliner", "spotify_url": "https://open.spotify.com/artist/73A3bLnfnz5BoQjb4gNCga", "instagram_handle": "@feelmybicep", "follower_count": 142050
| # | artist_id | name | role | bio | spotify_url | apple_music_url |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Promoter & Organizer objects from dice.fm. All fields typed and schema-versioned.
"promoter_id": "p-1102", "name": "Broadwick Live", "website": "https://broadwicklive.com", "active_events_count": 24, "past_events_count": 312, "follower_count": 8940, "is_verified": true
| # | promoter_id | name | description | contact_email | website | active_events_count |
|---|---|---|---|---|---|---|
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Our Dice.fm scraper captures the entire live event graph: from primary ticket availability and waitlist dynamics to venue coordinates and artist metadata, bypassing SPA complexities and mobile-first API constraints.
Event title, dates, times, age restrictions, and detailed descriptions scraped at scale across all supported cities.
Capture exact ticket tiers, pricing, booking fees, and availability flags timestamped per crawl.
Monitor when events hit sold-out status and track the activation of the Dice.fm Waitlist feature to gauge secondary demand.
Extract exact venue names, street addresses, and geolocation coordinates for spatial analysis.
Map headliners and support acts to their respective Spotify profiles, Apple Music links, and social media handles.
Track event volume, follower counts, and historical event data for specific promoters and event organizers.
Extract localized event feeds for London, New York, Los Angeles, Paris, and other major markets using geo-targeted proxies.
Monitor early bird, general release, and final release pricing structures to analyse promoter revenue models.
Run daily bulk exports for venue schedules or configure high-frequency pipelines for real-time ticket availability.
Brief in. Clean data out.
Provide target cities, specific venues, or promoter profiles. We design the exact JSON schema together.
We configure API reverse-engineering, proxy rotation, and session management tailored for Dice.fm architecture.
Schema validation, null-rate checks, and price parsing validation before full pipeline launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Dice.fm is a mobile-first platform with heavy React hydration and strict rate limits. Here is how we extract structured data reliably.
Rather than scraping heavy DOM structures, our pipeline intercepts the undocumented JSON API endpoints that power the Dice.fm mobile and web apps, ensuring faster extraction and cleaner data structures.
Dice.fm event discovery is heavily localized. We utilise residential proxies pinned to specific metropolitan areas (e.g., London, NYC) to ensure we see the exact event inventory a local user would see.
Ticket availability on Dice.fm fluctuates rapidly as users return tickets to the Waitlist. Our high-frequency crawlers capture these micro-changes, providing accurate demand signals.
For specific endpoints requiring complex session tokens generated via client-side JavaScript, we deploy headless Playwright instances to execute the React hydration and extract the necessary payload signatures.
For large event catalogues, we maintain a hash index of ticket availability per event. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Ticket brokers monitor sold-out status and Waitlist activation to price secondary inventory on platforms like StubHub or Ticketswap.
Real estate and hospitality analysts track event frequency and sold-out rates to estimate footfall for specific venues and neighborhoods.
Booking agents analyse historical ticket velocity for similar artists in specific cities to optimise future tour routing.
Event organizers track competitor pricing tiers, booking fees, and lineup curation to benchmark their own events.
Analysing the time delta between event announcement, ticket release, and Waitlist activation provides highly accurate demand forecasting models.
Record labels track support acts on fast-selling events to identify emerging talent with strong localized draw.
"Dice.fm holds the pulse of independent and electronic live music globally. Mapping its inventory reveals exact demand curves before secondary markets react."
Extracting data from a mobile-first, heavily React-hydrated platform requires more than simple HTTP GET requests. DataFlirt reverse-engineers undocumented API endpoints, manages geographic IP rotation to expose local event discovery, and monitors dynamic waitlist states at scale. We handle the complexity so you can focus on the analysis.
Everything supported by our dice.fm scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We bypass heavy DOM rendering by intercepting the exact JSON payloads the Dice.fm mobile app requests, resulting in faster extraction and zero layout-change breakages.
Pools of residential ISP proxies pinned to specific cities ensure we extract the exact localized event discovery feed for London, NYC, Paris, and beyond.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About dice.fm scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available event information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated event, venue, and pricing data. We do not extract personal user data or circumvent authentication walls to access purchased tickets.
We use city-specific residential ISP proxies and request timing modelled on human API consumption patterns. We monitor for 429 Too Many Requests responses in real time and trigger pool rotation automatically.
We support all major markets where Dice.fm operates, including London, Manchester, New York, Los Angeles, Chicago, Paris, Berlin, and Milan. We use geo-targeted IPs to extract the exact localized feed for each region.
Real-time streaming pipelines achieve sub-15-minute latency for ticket availability and waitlist status on a defined set of high-priority events. Full city schedules update daily.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per event detailing exactly when ticket tiers sell out and when the waitlist activates.
Our smallest packages start at tracking all events in a single major city with daily delivery. For multi-city tracking or sub-hourly waitlist monitoring, we price based on compute volume and delivery frequency.
Yes. We provide a sample run of up to 500 events for a specified city as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily dump of London venue schedules or continuous waitlist monitoring across 10,000 events, we scope, build, and operate the pipeline. Tell us what you need.
