We extract flight schedules, dynamic pricing tiers, seat availability, and route networks from flyasiana.com. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Schedules objects from flyasiana.com. All fields typed and schema-versioned.
"flight_number": "OZ201", "airline_code": "OZ", "origin": "LAX", "destination": "ICN", "departure_time": "2024-11-12T12:40:00", "arrival_time": "2024-11-13T17:35:00", "duration": "12h 55m", "aircraft_type": "A380-800"
| # | flight_number | airline_code | origin | destination | departure_time | arrival_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from flyasiana.com. All fields typed and schema-versioned.
"flight_number": "OZ201", "cabin_class": "Business Smartium", "fare_class": "J", "price": 3450.0, "currency": "USD", "taxes": 120.5, "total_price": 3570.5, "refundable": true
| # | flight_number | departure_date | fare_class | cabin_class | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seat Availability objects from flyasiana.com. All fields typed and schema-versioned.
"flight_number": "OZ201", "date": "2024-11-12", "cabin_class": "Economy", "available_seats": 42, "pitch": "33 inches", "width": "18 inches", "wifi_available": true
| # | flight_number | date | cabin_class | total_seats | available_seats | seat_map_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Details objects from flyasiana.com. All fields typed and schema-versioned.
"origin_airport": "ICN", "destination_airport": "JFK", "distance_miles": 6882, "frequency_per_week": 14, "codeshare_partners": "['UA', 'AC']", "seasonality": "Year-round"
| # | route_id | origin_airport | origin_city | destination_airport | destination_city | distance_miles |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Baggage & Policies objects from flyasiana.com. All fields typed and schema-versioned.
"fare_class": "Economy", "route_type": "Americas", "check_in_allowance": "2 pieces, 23kg each", "cabin_allowance": "1 piece, 10kg", "excess_fee_per_kg": 200, "excess_fee_currency": "USD", "pet_in_cabin_allowed": true
| # | fare_class | route_type | check_in_allowance | cabin_allowance | excess_fee_per_kg | excess_fee_currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Asiana Airlines scraper handles every layer of the booking engine: dynamic pricing, flexible date matrices, seat availability, and route intelligence with full session management.
Extract departure times, arrival times, aircraft types, and flight durations across the entire Asiana route network.
Capture base fares, taxes, and total prices for Economy, Business Smartium, and First Suite classes.
Track remaining seats per cabin class to estimate flight loads and booking velocity.
Extract pricing in KRW, USD, JPY, EUR, and other local currencies using geo-targeted residential proxies.
Map direct flights, layovers, and codeshare partnerships across the Star Alliance network.
Extract weight limits, piece concepts, and excess baggage fees specific to fare classes and routes.
Capture the flexible date matrix pricing grids to map out cheapest travel windows.
Separate base fare from carrier-imposed fuel surcharges and airport taxes.
Run one-off bulk exports or configure continuous pipelines at hourly cadences with change-detection diffing.
Track A380, A350, and B777 equipment deployment across specific routes and seasons.
Brief in. Clean data out.
Provide origin-destination pairs, travel dates, or cabin classes. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session token management, and bot circumvention for flyasiana.com.
Schema validation, null-rate checks, price anomaly detection, and route coverage verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines invest heavily in scraping detection to protect pricing data. Here is how we stay resilient.
Flyasiana uses advanced bot mitigation that blocks data center IPs. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management trained on real user behaviour.
Flight search requests require specific session tokens, CSRF tokens, and hidden form fields generated during the initial page load. We maintain these session states across the entire booking flow.
Pricing matrices and flexible date grids load asynchronously via internal API calls. We run full Playwright browser sessions with JavaScript execution to capture data that headless HTTP clients miss.
Fares change based on the search origin IP address. We route requests through specific country proxy pools to extract accurate point-of-sale pricing for different markets.
For large route networks, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Online travel agencies sync Asiana Airlines inventory, schedules, and pricing to maintain accurate flight search results.
Competitor airlines monitor transpacific fares and promotional discounts to adjust their own yield management strategies.
Booking tools update internal databases with accurate schedules and corporate fare availability for employee travel portals.
Aviation analysts track capacity, route frequency, and aircraft deployment to evaluate market share and operational efficiency.
Revenue teams correlate seat availability drops with booking velocity to improve pricing models and predict peak travel periods.
Track Asiana Club miles accrual rates across different fare buckets to analyse loyalty program structures.
"Airline pricing is the original dynamic market. Flyasiana fare structures shift constantly across regions, cabins, and booking windows, requiring persistent extraction."
Most engineering teams underestimate the complexity of scraping airline booking engines. Flyasiana relies on complex session states, geo-fenced pricing, and heavy bot mitigation. DataFlirt manages the residential proxies, token generation, and concurrent scheduling so your team can focus on yield analysis and route planning.
Everything supported by our flyasiana.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, session tokens, and interaction flows required by the booking engine.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions required for multi-step flight searches.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About flyasiana.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing data is generally permissible. DataFlirt targets only public, non-authenticated route and fare data. We do not extract PII or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions, and precise session token management. We monitor for rate limits in real time and trigger proxy rotation automatically.
Yes. We route requests through specific geographic proxy nodes to extract accurate point-of-sale pricing in KRW, USD, JPY, EUR, and other regional currencies.
Real-time streaming pipelines achieve sub-60-minute latency for specific origin-destination pairs. Full route network refreshes operate on daily or weekly cadences depending on volume.
Yes. We extract the remaining seat count per cabin class (Economy, Business Smartium, First Suite) to help you monitor flight loads.
Yes. We capture the full +/- 3 days pricing grid to map out the cheapest travel windows in a single extraction pass.
Our packages start with a defined list of origin-destination pairs and specific travel date ranges. Contact us with your route requirements for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full route network catalogue or continuous price monitoring across key origin-destination pairs, we build and operate the pipeline.
