We extract tour listings, departure dates, pricing signals, day-by-day itineraries, and guest reviews from Trafalgar. 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 Packages objects from trafalgar.com. All fields typed and schema-versioned.
"tour_id": "TR-EUR-102", "tour_name": "European Whirl", "region": "Europe", "duration_days": 12, "travel_style": "Discoveries", "base_price": 3295.0, "currency": "USD", "rating": 4.7
| # | tour_id | tour_name | region | countries_visited | duration_days | travel_style |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dates & Pricing objects from trafalgar.com. All fields typed and schema-versioned.
"tour_id": "TR-EUR-102", "departure_date": "2025-06-14", "return_date": "2025-06-25", "availability_status": "Available", "standard_price": 3295.0, "discounted_price": 2965.5, "discount_percentage": 10, "guaranteed_departure": true
| # | tour_id | departure_date | return_date | availability_status | seats_remaining | standard_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Itinerary Stops objects from trafalgar.com. All fields typed and schema-versioned.
"tour_id": "TR-EUR-102", "day_number": 3, "day_title": "Rome to Florence", "destinations": "['Rome', 'Florence']", "meals_included": "['Breakfast', 'Dinner']", "accommodation_name": "Grand Hotel Mediterraneo", "description": "Travel north through the rolling hills of Tuscany."
| # | tour_id | day_number | day_title | destinations | activities | meals_included |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Optional Experiences objects from trafalgar.com. All fields typed and schema-versioned.
"tour_id": "TR-EUR-102", "experience_name": "Tuscan Dinner and Music", "day_offered": 3, "price": 85.0, "currency": "EUR", "duration_hours": 3.5, "description": "Enjoy traditional Tuscan cuisine with local wine and live music."
| # | tour_id | experience_id | experience_name | day_offered | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Guest Reviews objects from trafalgar.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "tour_id": "TR-EUR-102", "reviewer_name": "Sarah J.", "review_date": "2024-08-12", "overall_rating": 5, "director_rating": 5, "review_title": "Trip of a lifetime", "review_body": "The travel director was exceptional. Every detail was handled."
| # | review_id | tour_id | reviewer_name | review_date | travel_date | overall_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Trafalgar scraper handles dynamic pricing widgets, nested day-by-day itineraries, and AJAX-loaded departure dates with full JavaScript rendering and session management built in.
Tour name, duration, travel style, activity level, region, and total countries visited scraped across the entire catalogue.
Extract all upcoming departure dates, return dates, guaranteed departure flags, and real-time seat availability.
Capture base prices, discounted rates, early booking deals, and past guest offers across multiple currencies.
Parse nested itinerary structures including daily destinations, activities, included meals, and accommodation details.
Extract full review text, overall ratings, travel director ratings, and travel dates paginated across all reviews.
Scrape add-on excursions, pricing, durations, and descriptions tied to specific days on the itinerary.
Extract hotel names, locations, and quality ratings provided for each night of the tour.
Scrape localized pricing and availability targeted at US, UK, Australian, or European source markets.
Run one-off bulk exports or configure continuous pipelines at daily or weekly cadences to track price fluctuations.
Brief in. Clean data out.
Provide target regions, specific tour URLs, or full catalogue requirements. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and dynamic content hydration for trafalgar.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Travel sites rely heavily on dynamic pricing and complex DOM structures. Here is how we ensure reliable data extraction.
Trafalgar loads pricing, availability, and departure dates asynchronously via API calls after the initial page load. We run full Playwright browser sessions to trigger these requests and capture the hydrated data.
Day-by-day itineraries contain mixed content types: text descriptions, structured meal lists, and accommodation blocks. Our parsers normalise this unstructured DOM into clean, relational JSON arrays.
Pricing and availability change based on the user's location. We route requests through residential proxies in your target market to capture the exact pricing your customers see.
Travel operators update site layouts frequently for seasonal campaigns. We use multiple fallback chains per field so a layout change does not break your data pipeline.
We maintain a hash index of last-seen values per tour date. Subsequent runs only push diffs, allowing you to track yield management and price drops over time without processing duplicate data.
Rival tour operators monitor Trafalgar's pricing, discount strategies, and new itinerary launches to adjust their own product positioning.
Travel agencies track yield management patterns and early booking discounts to advise clients on the best time to purchase.
Analysts track itinerary popularity, regional focus shifts, and review sentiment to identify emerging travel trends.
LLM developers ingest structured itineraries and optional experiences to train conversational travel planning agents.
Correlate guaranteed departure flags and sold-out statuses with regional events to predict travel demand.
Metasearch engines normalise Trafalgar tour data to display alongside competing products from other operators.
"Trafalgar holds some of the richest guided travel data available, but extracting daily itineraries and dynamic pricing requires serious infrastructure."
Most teams underestimate the complexity of travel site extraction. Reliable scraping requires residential proxies, full JavaScript rendering for pricing widgets, and complex DOM parsing for nested itineraries. DataFlirt absorbs that complexity so your engineers can focus on product development, not pipeline maintenance.
Everything supported by our trafalgar.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, triggering the AJAX calls required for pricing and availability data.
We maintain pools of residential ISP proxies across major markets. This allows us to extract the exact pricing and inventory targeted at US, UK, or Australian consumers.
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 trafalgar.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Trafalgar is generally permissible. DataFlirt targets only public, non-authenticated tour, pricing, and itinerary data. We do not extract personal data or circumvent authentication walls. Clients should review Trafalgar's ToS and consult legal counsel for specific use cases.
We use full Playwright browser sessions to execute JavaScript and wait for network idle states. This ensures all pricing widgets, departure dates, and availability statuses are fully populated before extraction.
Yes. We route requests through residential proxies located in your target market (e.g., US, UK, Australia) to capture the localized pricing and currency displayed to consumers in those regions.
We can configure pipelines to run at daily or weekly cadences depending on your requirements. Full catalogue refreshes typically complete within 2 to 4 hours.
Yes. Every pipeline run produces timestamped snapshots. We can deliver diff files that only contain tours where the price or availability has changed since the previous run.
Yes. We parse the nested itinerary structures into relational arrays, capturing the day number, destinations visited, included meals, accommodation details, and text descriptions for every day of the tour.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across all departure dates, we build and operate the pipeline. Tell us what you need.
