We extract event listings, session schedules, speaker biographies, and sponsor directories from Whova. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery.
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 whova.com. All fields typed and schema-versioned.
"event_id": "whv_88392", "name": "Global Tech Summit 2026", "organizer": "TechForward Inc.", "start_date": "2026-09-14", "end_date": "2026-09-16", "location": "London, UK", "format": "Hybrid", "category": "Technology"
| # | event_id | name | organizer | start_date | end_date | location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Session Agendas objects from whova.com. All fields typed and schema-versioned.
"session_id": "ses_4921", "event_id": "whv_88392", "title": "Scaling Distributed Databases", "start_time": "2026-09-14T10:00:00Z", "end_time": "2026-09-14T11:00:00Z", "track": "Infrastructure", "room": "Hall B"
| # | session_id | event_id | title | start_time | end_time | track |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Speaker Profiles objects from whova.com. All fields typed and schema-versioned.
"speaker_id": "spk_9912", "name": "Jane Doe", "title": "Principal Engineer", "company": "DataFlirt", "bio": "Jane leads data extraction architecture...", "linkedin_url": "https://linkedin.com/in/janedoe", "session_ids": "['ses_4921']"
| # | speaker_id | name | title | company | bio | linkedin_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sponsors & Exhibitors objects from whova.com. All fields typed and schema-versioned.
"sponsor_id": "spn_331", "name": "CloudScale Systems", "tier": "Platinum", "booth_number": "A12", "website_url": "https://cloudscale.example.com", "contact_email": "hello@cloudscale.example.com"
| # | sponsor_id | name | tier | booth_number | description | website_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ticketing & Pricing objects from whova.com. All fields typed and schema-versioned.
"ticket_id": "tkt_882", "event_id": "whv_88392", "name": "Early Bird General Admission", "price": 299.0, "currency": "USD", "sales_start": "2026-01-01T00:00:00Z", "sales_end": "2026-05-31T23:59:59Z"
| # | ticket_id | event_id | name | price | currency | sales_start |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Whova pipeline navigates complex event hierarchies, extracting interconnected data across agendas, speakers, and sponsors without manual intervention.
Extract event names, dates, locations, formats, and descriptions across public Whova listings.
Capture session titles, start times, tracks, room allocations, and descriptions for multi-day programmes.
Extract speaker biographies, job titles, company affiliations, and social links mapped to specific sessions.
Collect sponsor names, tier levels, booth locations, and corporate descriptions from event directories.
Monitor ticket tiers, pricing curves, availability windows, and currency variations.
Maintain primary and foreign keys linking speakers to sessions, and sessions to events.
Extract structured venue names, addresses, and virtual meeting links.
Run scheduled pipelines to capture agenda updates and new speaker announcements as events approach.
Extract data across all geographic regions and event categories hosted on the Whova platform.
Brief in. Clean data out.
Provide target event URLs, categories, or search parameters. We map the required schema.
We configure Playwright crawlers, handle SPA navigation, and implement request concurrency limits.
Schema validation, relation integrity checks, and data type normalisation before full execution.
JSON, CSV, or Parquet pushed to your S3 bucket or data warehouse on your defined schedule.
Modern event platforms rely on heavy client-side rendering and complex API structures. We manage the extraction complexity.
Whova relies heavily on React and dynamic state hydration. We use Playwright to execute JavaScript, wait for network idle states, and extract data directly from the rendered DOM or intercepted API responses.
Agendas and speaker lists often use infinite scroll or dynamic pagination. Our crawlers simulate human scrolling behaviour to trigger lazy-loaded content and capture complete lists.
Event platforms implement strict IP rate limits. We distribute requests across residential proxy pools and introduce randomised delays to maintain high extraction throughput without triggering blocks.
Speakers, sessions, and sponsors are interconnected. Our pipeline maintains relational integrity, ensuring speaker IDs match session assignments perfectly in the final output.
Whova updates its frontend layout frequently. We use heuristic matching and fallback selectors to ensure data extraction continues without interruption when DOM structures change.
Sales teams extract sponsor and exhibitor lists to build targeted account lists for industry-specific campaigns.
Event organisers monitor competing events, tracking speaker line-ups, pricing tiers, and sponsor acquisition.
Content teams aggregate speaker profiles across multiple events to identify thought leaders for their own conferences.
Analysts track event volume, formats (virtual vs physical), and topic trends to forecast industry growth.
Hospitality groups monitor event locations and dates to predict local accommodation and venue demand.
Industry portals ingest Whova event data to populate comprehensive industry calendars and newsletters.
"Whova hosts the most concentrated B2B event data available, but extracting structured agendas and sponsor lists requires navigating complex SPA architecture."
Event platforms deploy aggressive rate limiting and dynamic DOM structures. DataFlirt manages the residential proxies, JavaScript rendering, and schema updates so your engineering team receives clean data without maintaining fragile scrapers.
Everything supported by our whova.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 and retry logic. Playwright manages JavaScript execution and SPA state hydration.
We route requests through residential ISP proxies, preventing IP bans and maintaining high throughput.
Airflow schedules extraction runs, manages dependencies, and triggers delivery to your specified endpoints.
Data delivered to where your team already works — no new tooling required.
About whova.com scraping, legality, and pipeline operations.
Ask us directly →No. We only extract publicly available information from Whova event pages. We do not circumvent authentication walls or extract data from ticket-gated private events.
Our pipelines use heuristic matching and multiple fallback selectors. If a DOM change breaks extraction, our monitoring alerts us immediately, and we deploy a fix within hours.
Yes. We can configure delta pipelines to run daily or weekly, capturing schedule adjustments, new speakers, and room changes.
No. Attendee lists, private networking messages, and community board discussions are strictly gated and fall outside our public data extraction policy.
We provide relational data. Speakers are mapped to sessions, and sessions are mapped to events using unique identifiers, allowing you to reconstruct the full event graph.
Delivery frequency is configurable. We support one-off historical extractions, weekly syncs, or daily delta updates depending on your requirements.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop manually copying event agendas. We build and maintain the extraction pipeline, delivering structured Whova data directly to your infrastructure.
