We extract event schedules, dynamic ticket pricing, availability signals, and organizer profiles from Universe. 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 universe.com. All fields typed and schema-versioned.
"event_id": "64a2b19f", "title": "Tech Innovators Summit 2026", "start_time": "2026-09-15T09:00:00Z", "timezone": "America/New_York", "venue_name": "Javits Center", "city": "New York", "status": "active", "category": "Technology"
| # | event_id | title | description | start_time | end_time | timezone |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ticket Tiers objects from universe.com. All fields typed and schema-versioned.
"ticket_id": "tkt_88219", "tier_name": "Early Bird General Admission", "price": 149.0, "currency": "USD", "quantity_total": 500, "quantity_available": 12, "is_sold_out": false, "sale_end": "2026-08-01T23:59:59Z"
| # | ticket_id | event_id | tier_name | price | currency | quantity_total |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Organizer Profiles objects from universe.com. All fields typed and schema-versioned.
"organizer_id": "org_9122", "name": "Global Tech Events", "total_events": 45, "followers": 1240, "website": "https://globaltechevents.example.com", "contact_email": "hello@globaltechevents.example.com", "profile_url": "https://www.universe.com/users/global-tech"
| # | organizer_id | name | profile_url | description | total_events | followers |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Add-ons & Merchandise objects from universe.com. All fields typed and schema-versioned.
"addon_id": "add_441", "name": "VIP Parking Pass", "price": 45.0, "currency": "USD", "available": true, "max_per_order": 1, "type": "parking", "event_id": "64a2b19f"
| # | addon_id | event_id | name | description | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search & Discovery objects from universe.com. All fields typed and schema-versioned.
"keyword": "tech conference", "location": "New York", "position": 3, "event_id": "64a2b19f", "is_promoted": false, "min_price": 149.0, "max_price": 499.0, "scraped_at": "2026-05-12T10:05:00Z"
| # | keyword | location | position | event_id | title | date_string |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Universe scraper handles every layer of the platform: event listings, dynamic ticketing widgets, organizer profiles, and availability signals - with JavaScript rendering, session management, and anti-bot circumvention built in.
Title, description, schedules, timezone data, and status indicators scraped directly from the Universe event pages.
Capture General Admission, VIP, student rates, and dynamic pricing tiers along with currency and sale windows.
Track sold out status, capacity limits, and remaining ticket quantities to gauge demand and event popularity.
Extract organizer profiles, historical event counts, follower metrics, and external contact information.
Parse venue names, street addresses, cities, and countries to map event density across geographic regions.
Extract upsell items like parking passes, VIP upgrades, and merchandise tied to specific event listings.
Categorise events by Universe's internal taxonomy, including workshops, concerts, conferences, and networking events.
Handle international events with native currency extraction, ensuring accurate financial modelling across borders.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide Universe URLs, category filters, location parameters, or organizer IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for universe.com.
Schema validation, null-rate checks, price-outlier detection, and sample payloads before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Ticketing platforms invest heavily in scraping detection to protect inventory data. Here is how we stay resilient.
Ticketing sites block data center IPs aggressively. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Universe ticket availability and pricing tiers are loaded dynamically via JavaScript. We run full Playwright browser sessions to hydrate these widgets and extract the underlying JSON payloads.
Universe updates its frontend framework regularly. Our selector strategy uses multiple fallback chains per field, including Next.js data props extraction, so layout changes do not break your data pipeline.
For tracking ticket availability over time, we maintain a hash index of last-seen values per tier. Subsequent runs only push diffs, reducing downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, availability drops, schema drift, and coverage gaps.
Ticket brokers track primary availability and sold-out signals to optimise pricing on secondary resale markets.
Event organizers monitor competitor pricing, ticket tiers, and capacity limits in specific geographic markets.
Real estate and urban planners track event density and venue booking rates to assess commercial property value.
Hotels and airlines correlate local event schedules and expected attendance with future travel demand.
Hedge funds track aggregate ticketing volume and pricing trends as leading indicators for the live entertainment sector.
Local discovery apps and event directories populate their platforms with structured schedule and pricing data.
"Universe holds a massive repository of global live event data - but tracking dynamic ticket availability and pricing requires continuous, distributed execution."
Most teams underestimate the investment required: reliable Universe scraping requires residential proxies, full JavaScript rendering for ticketing widgets, CAPTCHA handling, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis - not the infrastructure.
Everything supported by our universe.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 universe.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Universe is generally permissible under applicable law. DataFlirt targets only public, non-authenticated event, pricing, and organizer data. We do not extract personal attendee data or circumvent authentication walls.
We use full Playwright browser sessions to execute JavaScript, triggering the dynamic load of ticketing tiers and availability data, capturing the underlying JSON responses directly from the browser.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per event for ticket tier availability, price changes, and sold-out status from the date your pipeline starts.
We extract publicly listed contact information, such as website URLs, social media links, and support emails displayed on the public organizer profile pages.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined event set. Full category refreshes complete within a 6-12 hour window.
Our smallest packages start at a defined event list (typically 1,000-10,000 events) with weekly delivery. For larger catalogues, we price based on volume and delivery frequency.
Yes. We provide a sample run of up to 500 events 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 one-off event dump or continuous availability monitoring across 50,000 events - we scope, build, and operate the pipeline. Tell us what you need.
