We extract multi-day tour itineraries, operator intelligence, dynamic availability pricing, and customer reviews from TourRadar. 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 Tour Metadata objects from tourradar.com. All fields typed and schema-versioned.
"tour_id": "T84921", "title": "Everest Base Camp Trek", "operator": "Intrepid Travel", "duration_days": 15, "start_location": "Kathmandu, Nepal", "physical_rating": "Challenging", "max_group_size": 12, "travel_style": "Active"
| # | tour_id | title | operator | duration_days | start_location | end_location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Availability objects from tourradar.com. All fields typed and schema-versioned.
"tour_id": "T84921", "departure_date": "2026-09-14", "return_date": "2026-09-28", "base_price": 1450.0, "discounted_price": 1305.0, "discount_pct": 10, "currency": "USD", "availability_status": "Available", "availability_count": 4
| # | tour_id | departure_date | return_date | base_price | discounted_price | discount_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Daily Itinerary objects from tourradar.com. All fields typed and schema-versioned.
"tour_id": "T84921", "day_number": 3, "day_title": "Trek to Namche Bazaar", "day_description": "We begin our trek following the Dudh Kosi river...", "accommodation_included": "Teahouse", "meals_included": "['Breakfast', 'Dinner']", "activities_included": "['Sagarmatha National Park Entry']", "travel_time_hours": 6.5
| # | tour_id | day_number | day_title | day_description | accommodation_included | meals_included |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inclusions & Exclusions objects from tourradar.com. All fields typed and schema-versioned.
"tour_id": "T84921", "guide_type": "English speaking local guide", "meals_summary": "14 breakfasts, 0 lunches, 14 dinners", "transport_summary": "Plane, Trekking", "accommodation_summary": "Teahouse (12 nts), Hotel (2 nts)", "flights_included": false, "insurance_required": true
| # | tour_id | guide_type | meals_summary | transport_summary | accommodation_summary | flights_included |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from tourradar.com. All fields typed and schema-versioned.
"review_id": "REV991823", "tour_id": "T84921", "overall_rating": 4.8, "guide_rating": 5.0, "itinerary_rating": 4.5, "review_text": "An incredible experience. Our guide Pasang was exceptional.", "traveller_type": "Solo Traveller", "review_date": "2025-11-04"
| # | review_id | tour_id | reviewer_name | review_date | overall_rating | guide_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our TourRadar scraper handles the entire platform: dynamic availability calendars, complex multi-day itineraries, operator metadata, and paginated review sets - bypassing bot protections automatically.
Extract daily schedules, accommodation types, included meals, and optional activities mapped precisely to the day number.
Capture departure dates, return dates, seasonal pricing fluctuations, and remaining seat counts rendered via JavaScript.
Track offerings from G Adventures, Intrepid, and thousands of local operators. Monitor their portfolio size and pricing strategies.
Structured extraction of what is covered: meal counts, transport modes, flight inclusions, and insurance requirements.
Collect overall ratings alongside specific sub-ratings for guides, transport, and itineraries across paginated review histories.
Extract exact start and end cities, total countries visited, and intermediate stops to build geospatial routing models.
Categorise tours by physical exertion levels, minimum/maximum age limits, and target traveller styles.
Capture pricing in localized currencies to monitor regional price discrimination and exchange rate adjustments.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or weekly cadences.
Brief in. Clean data out.
Provide region URLs, operator lists, or search parameters. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management tailored for TourRadar.
Schema validation, null-rate checks, and calendar availability verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
TourRadar uses dynamic client-side rendering for availability and pricing. Here is how we extract it reliably.
TourRadar's departure dates and prices are rendered dynamically via JavaScript based on user session state. We use Playwright to hydrate the calendar widgets and extract the full matrix of upcoming departures.
Frequent scraping of pricing endpoints triggers rate limits. We distribute requests across residential IP pools with localized headers to ensure uninterrupted access to regional pricing data.
Tour pages have highly variable layouts depending on the operator. We use multi-layered extraction rules to normalise daily itineraries, regardless of how the operator formatted their text.
For large catalogues, we maintain a hash index of availability dates. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs. We alert on null-rate spikes in pricing fields or missing itinerary days, ensuring data completeness.
Online travel agencies monitor pricing, discount strategies, and operator coverage to ensure competitive parity.
Tour operators analyse competitor itineraries, inclusion standards, and pricing models to design better travel products.
Revenue managers adjust local pricing based on TourRadar availability metrics and seasonal demand signals.
Meta-search engines enrich their own databases with structured itinerary data and operator reviews.
Hospitality groups analyse guide performance and accommodation feedback across thousands of tours to identify quality trends.
Market analysts identify growing travel regions and shifting demographic preferences based on new tour listings.
"TourRadar aggregates the world's multi-day tours, but standardising thousand-page itineraries into queryable tables requires dedicated extraction infrastructure."
Most teams underestimate the investment required: reliable TourRadar scraping requires residential proxies, full JavaScript rendering for availability calendars, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our tourradar.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 deduplication. Playwright handles JavaScript rendering for availability calendars. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request to prevent IP bans and capture correct regional pricing.
Pipelines run on AWS Lambda and ECS. 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 tourradar.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from TourRadar is generally permissible under applicable law. DataFlirt targets only public, non-authenticated tour, pricing, and review data. We do not extract personal user data or circumvent authentication walls.
We use Playwright to execute the client-side JavaScript that renders the availability widgets, capturing the full matrix of departure dates, prices, and remaining seats.
Yes. We extract daily schedules as nested JSON arrays, parsing out day numbers, titles, descriptions, included meals, and accommodation types.
We can configure pipelines to run daily or weekly depending on your needs. For specific high-priority tours, we can run intra-day checks.
Yes. We capture the exact currency and numeric value displayed based on the regional proxy used during the crawl.
Our smallest packages start at a defined geographic region or operator list with weekly delivery. We price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 100 tours as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous availability feed across 50,000 tours, we scope, build, and operate the pipeline. Tell us what you need.
