We extract flight schedules, hotel rates, package tours, and dynamic pricing signals from Fliggy. 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 Itineraries objects from fliggy.com. All fields typed and schema-versioned.
"flight_number": "CZ3001", "airline": "China Southern", "departure_airport": "CAN", "arrival_airport": "PEK", "price": 1250.0, "currency": "CNY", "cabin_class": "Economy", "available_seats": 4
| # | flight_id | airline | flight_number | departure_airport | arrival_airport | departure_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Hotel Rates objects from fliggy.com. All fields typed and schema-versioned.
"hotel_id": "H-982734", "hotel_name": "Shangri-La Guangzhou", "star_rating": 5, "room_type": "Deluxe River View", "price_per_night": 1450.0, "currency": "CNY", "user_rating": 4.8, "review_count": 3412
| # | hotel_id | hotel_name | city | star_rating | room_type | price_per_night |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Package Tours objects from fliggy.com. All fields typed and schema-versioned.
"package_id": "PT-44812", "title": "5 Days Sanya Luxury Resort Package", "destination": "Sanya", "duration_days": 5, "price": 3299.0, "currency": "CNY", "operator_name": "Fliggy Direct", "rating": 4.7
| # | package_id | title | destination | duration_days | duration_nights | inclusions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Train Schedules objects from fliggy.com. All fields typed and schema-versioned.
"train_number": "G101", "train_type": "High-Speed", "departure_station": "Beijing South", "arrival_station": "Shanghai Hongqiao", "seat_type": "Second Class", "price": 553.0, "currency": "CNY", "tickets_left": 12
| # | train_number | train_type | departure_station | arrival_station | departure_time | arrival_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Attractions objects from fliggy.com. All fields typed and schema-versioned.
"ticket_id": "TK-99381", "attraction_name": "Shanghai Disneyland", "ticket_type": "1-Day Standard Ticket", "price": 475.0, "currency": "CNY", "instant_confirmation": true, "rating": 4.9, "booking_count": 158200
| # | ticket_id | attraction_name | city | ticket_type | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Fliggy scraper navigates Alibaba's complex frontend architecture to extract structured travel inventory, pricing, and availability records.
Extract domestic and international flight schedules, airline operators, layover details, and aircraft types across Fliggy's entire route network.
Monitor nightly rates, room availability, and cancellation terms across millions of properties. Capture dynamic pricing changes in real time.
Track fare fluctuations, Alipay promotional discounts, and member-only pricing tiers to build accurate historical pricing models.
Extract comprehensive train schedules, seat class availability, and pricing for mainland China routes and cross-border connections.
Capture bundled itineraries, inclusions, operator details, and customer reviews for vacation packages and group tours.
Extract pricing, validity rules, and booking volumes for theme parks, museums, and local experiences listed on the platform.
Monitor international travel corridors, visa requirements listed by operators, and multi-airline connecting flights.
Extract user-generated content, aggregate ratings, and textual reviews for hotels and tour operators to measure customer sentiment.
Run extractions at hourly or daily cadences with change-detection diffing to monitor fast-moving inventory and flash sales.
Brief in. Clean data out.
Provide origin-destination pairs, hotel IDs, or city codes. We design the extraction schema together.
We configure Scrapy crawlers, Alibaba WAF bypasses, proxy rotation, and Playwright rendering for fliggy.com.
Schema validation, null-rate checks, price-outlier detection, and sample data review before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Alibaba invests heavily in scraping detection. Here is how we maintain reliable access to Fliggy's travel inventory.
Fliggy sits behind Alibaba's aggressive Web Application Firewall. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full TLS spoofing to blend with normal user traffic.
Fliggy's search results and pricing matrices are heavily JavaScript-rendered. We run full Playwright browser sessions to execute scripts, trigger lazy-loading, and capture the final state of dynamic fare displays.
Pricing often varies by region or requires specific session tokens. We maintain localized proxy pools and handle complex cookie lifecycles to ensure accurate, region-specific fare extraction.
Fliggy frequently updates its UI components. Our selector strategy uses fallback chains combining CSS, XPath, and JSON-in-HTML parsing so layout shifts do not break the data pipeline.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing price fields, and coverage drops to resolve issues before they impact your downstream systems.
Online travel agencies monitor Fliggy fares to ensure price parity and adjust their own markups dynamically.
Airlines and hotel chains track competitor pricing and inventory levels on Fliggy to optimise their yield management systems.
Tourism boards and analysts track route popularity, package tour volumes, and pricing trends to gauge regional travel demand.
Data science teams use historical fare datasets to train predictive models for flight and hotel price forecasting.
Logistics and hospitality suppliers correlate travel booking volumes with future local demand for goods and services.
Private equity firms track active listings, operator growth, and pricing power to evaluate investments in the APAC travel sector.
"Fliggy aggregates the most comprehensive travel inventory for the APAC region but extracting that pricing data requires bypassing aggressive Alibaba anti-bot systems."
Most teams underestimate the investment required: reliable Fliggy scraping requires regional residential proxies, full JavaScript rendering for dynamic pricing modules, and continuous anomaly monitoring to detect WAF blocks. DataFlirt absorbs that complexity so your engineers can focus on the analysis rather than the infrastructure.
Everything supported by our fliggy.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, cookie sessions, and interaction flows required by Fliggy's SPA architecture.
We maintain pools of residential ISP proxies across APAC regions. Rotation happens per-request with sticky sessions where required to maintain consistent pricing views.
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 fliggy.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and schedule information is generally permissible under applicable laws for public data. DataFlirt targets only public, non-authenticated travel inventory. We do not extract personal data or circumvent authentication walls to access private accounts. Clients should consult legal counsel for specific jurisdictional use cases.
We use regional residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for WAF blocks in real time and trigger pool rotation automatically.
Yes. We configure high-frequency pipelines to monitor specific origin-destination pairs or hotel properties, capturing intra-day price fluctuations and availability drops.
We extract the publicly visible promotional tiers and discounts displayed on the listing pages. User-specific targeted promotions requiring authenticated sessions are not supported.
Our smallest packages start at a defined route or property list (typically 1,000 to 10,000 entities) with daily delivery. For larger catalogues, we price based on volume and frequency.
Yes. We extract international flight itineraries, including multi-carrier connections, layover durations, and cross-border pricing displayed on Fliggy.
Yes. We provide a sample run of up to 500 routes or properties 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 hotel catalogue dump or a continuous fare-monitoring feed across 10,000 routes - we scope, build, and operate the pipeline. Tell us what you need.
