We extract flight itineraries, hotel inventory, dynamic pricing signals, transit routes, and user reviews from Trip.com. 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 Flight Data objects from trip.com. All fields typed and schema-versioned.
"flight_number": "SQ322", "airline": "Singapore Airlines", "departure_airport": "SIN", "arrival_airport": "LHR", "price": 842.5, "currency": "USD", "stops": 0, "cabin_class": "Economy"
| # | flight_id | airline | flight_number | departure_airport | arrival_airport | departure_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Hotel Inventory objects from trip.com. All fields typed and schema-versioned.
"hotel_id": "H-98213", "name": "Marina Bay Sands", "star_rating": 5, "price_per_night": 450.0, "currency": "USD", "user_rating": 4.7, "review_count": 24190, "tax_included": false
| # | hotel_id | name | star_rating | location | coordinates | check_in_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Transit & Trains objects from trip.com. All fields typed and schema-versioned.
"transit_type": "Train", "operator": "Eurostar", "departure_station": "St Pancras", "arrival_station": "Gare du Nord", "price": 115.0, "currency": "EUR", "seat_class": "Standard Premier", "availability_status": "Available"
| # | route_id | transit_type | operator | departure_station | arrival_station | departure_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for User Reviews objects from trip.com. All fields typed and schema-versioned.
"review_id": "R-773821", "rating_overall": 4.8, "travel_type": "Business", "review_title": "Excellent service", "review_date": "2026-03-14", "author_country": "UK", "rating_cleanliness": 5.0, "rating_location": 4.5
| # | review_id | property_id | property_type | author_name | author_country | travel_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Policies objects from trip.com. All fields typed and schema-versioned.
"entity_type": "Hotel_Room", "base_price": 210.0, "taxes": 24.5, "total_price": 234.5, "currency": "USD", "trip_coins_reward": 150, "cancellation_deadline": "2026-05-10T14:00:00Z", "meal_included": "Breakfast"
| # | entity_id | entity_type | base_price | taxes | total_price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Trip.com scraper handles every layer of the platform: dynamic flight pricing, hotel inventory grids, transit schedules, and user reviews. We manage JavaScript rendering, session state, and anti-bot circumvention natively.
Track schedules, airlines, layovers, and cabin classes across global routes directly from search grids.
Capture fluctuating prices for flights and hotels, including taxes, fees, and multi-currency conversions.
Extract room types, bed configurations, amenities, and real-time availability status for any property.
Monitor transit routes, operators, travel times, and seat availability across Europe and Asia.
Extract user reviews, sub-ratings for cleanliness and location, travel context, and photo attachments.
Parse complex ticket conditions, refund deadlines, and penalty fees for accurate risk modelling.
Track loyalty program incentives, exclusive discounts, and member-only pricing signals.
Access localized pricing and inventory across different Trip.com country domains and languages.
Run daily inventory audits or configure high-frequency pipelines for real-time price parity checks.
Brief in. Clean data out.
Provide route pairs, destination cities, or hotel IDs. We design the extraction schema together.
We configure Scrapy crawlers, Playwright sessions, proxy rotation, and CAPTCHA handling for trip.com.
Schema validation, null-rate checks, price-outlier detection, and timezone normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Trip.com heavily restricts automated access to protect partner pricing. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Trip.com alters pricing based on the user IP location. Our crawlers use region-specific residential proxies to capture accurate, localized pricing without triggering rate limits.
Flight and hotel searches rely heavily on asynchronous API calls and client-side rendering. We run full Playwright browser sessions to hydrate search results and capture dynamic pricing widgets.
Travel booking interfaces change frequently. Our selector strategy uses fallback chains and intercepts underlying API payloads where possible, ensuring layout updates do not break your pipeline.
Trip.com displays local times and currencies. Our pipelines normalise all timestamps to UTC and standardise currency codes, reducing downstream transformation overhead for your engineering team.
For large hotel catalogues, we maintain a hash index of last-seen values. Subsequent runs only push price or availability diffs, reducing compute cost and storage bloat.
OTAs and hotel chains monitor Trip.com to ensure their direct booking rates remain competitive across different regions.
Airlines and revenue managers analyse competitor flight pricing, layover structures, and cabin class availability to optimise their own fares.
Travel aggregators track route popularity, new transit connections, and hotel saturation to identify underserved markets.
Hospitality brands extract reviews and sub-ratings to benchmark guest satisfaction against local competitors.
Supply chain and logistics teams correlate transit frequency and hotel booking velocity with regional economic activity.
ML teams use structured itinerary and pricing datasets to train travel recommendation engines and fare prediction models.
"Trip.com aggregates global travel inventory into a single interface, but extracting that pricing and route data reliably requires navigating severe geographic rate limits."
Most teams underestimate the investment required: reliable Trip.com scraping requires region-specific residential proxies, full JavaScript rendering, timezone normalisation, and constant selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our trip.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 handles asynchronous flight searches, dynamic rendering, and interaction flows.
We maintain pools of residential ISP proxies across global regions. This allows us to bypass geographic blocks and capture accurate local pricing.
Pipelines run on AWS Lambda for burst loads and ECS for sustained loads. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About trip.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available travel data is generally permissible under applicable law. DataFlirt targets only public, non-authenticated flight, hotel, and transit data. We do not extract personal data or bypass authentication walls.
We use region-specific residential ISP proxies and full Playwright browser sessions with realistic fingerprints. We monitor for rate limits and CAPTCHAs, triggering pool rotation automatically.
Yes. Trip.com alters prices based on user location. We route requests through proxies in your target region to ensure the pricing data matches what local users see.
Real-time streaming pipelines achieve sub-15-minute latency for flight and hotel price checks. Bulk catalogue refreshes run on daily or weekly cadences depending on volume.
Yes. We extract the full price breakdown, including base fare, taxes, platform fees, and any applied discounts, ensuring your pricing models are accurate.
Our smallest packages start at defined route pairs or specific hotel lists with weekly delivery. For global tracking, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 100 flight routes or 50 hotel listings as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need daily hotel inventory audits or continuous flight price monitoring, we scope, build, and operate the pipeline. Tell us what you need.
