We extract flight schedules, dynamic pricing signals, ancillary fees, and route availability from AirAsia X. 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 Schedules objects from airasia.x. All fields typed and schema-versioned.
"flight_number": "D7 504", "airline_code": "D7", "origin_airport": "KUL", "destination_airport": "ICN", "departure_time": "2026-08-14T23:15:00Z", "arrival_time": "2026-08-15T06:45:00Z", "duration_minutes": 390, "stops": 0
| # | flight_number | airline_code | origin_airport | destination_airport | departure_time | arrival_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Fares objects from airasia.x. All fields typed and schema-versioned.
"flight_number": "D7 504", "departure_date": "2026-08-14", "fare_class": "Economy", "base_fare": 850.0, "taxes": 125.5, "total_fare": 975.5, "currency": "MYR", "fare_family": "Value Pack"
| # | flight_number | departure_date | fare_class | base_fare | taxes | total_fare |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Ancillary Services objects from airasia.x. All fields typed and schema-versioned.
"flight_number": "D7 504", "route": "KUL-ICN", "baggage_20kg_price": 120.0, "baggage_30kg_price": 180.0, "meal_standard_price": 25.0, "seat_hot_price": 150.0, "quiet_zone_price": 180.0, "currency": "MYR"
| # | flight_number | route | baggage_20kg_price | baggage_25kg_price | baggage_30kg_price | meal_standard_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Route Availability objects from airasia.x. All fields typed and schema-versioned.
"origin": "KUL", "destination": "ICN", "travel_date": "2026-08-14", "is_available": true, "seats_remaining": 4, "status": "Scheduled", "frequency_per_week": 7
| # | origin | destination | travel_date | is_available | seats_remaining | status |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Fleet & Aircraft objects from airasia.x. All fields typed and schema-versioned.
"aircraft_code": "A333", "aircraft_model": "Airbus A330-300", "configuration": "2-Class", "total_seats": 377, "premium_flatbed_seats": 12, "economy_seats": 365, "wifi_equipped": true
| # | aircraft_code | aircraft_model | configuration | total_seats | premium_flatbed_seats | economy_seats |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our AirAsia X scraper handles every layer of the booking engine: dynamic fares, ancillary pricing, seat availability, and multi-leg routes - with session management and anti-bot circumvention built in.
Flight numbers, departure times, arrival times, aircraft types, and operating carriers scraped across all active routes.
Capture base fares, taxes, and total prices across different fare families like Value Pack and Premium Flex.
Extract dynamic pricing for checked baggage tiers, in-flight meals, standard seat selection, and Hot Seats.
Scrape fares in local departure currencies and normalise to your base currency using daily exchange rates.
Map all active origin-destination pairs, direct flights, and Fly-Thru transit options across the network.
Monitor low-stock warnings and remaining seat counts to model demand curves and load factors.
Identify zero-fare promotions, BIG Sale events, and discounted fare buckets instantly.
Access point-of-sale specific pricing by routing requests through country-specific residential proxies.
Run one-off route exports or configure continuous pipelines at hourly or daily cadences.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, or network parameters. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for airasia.x.
Schema validation, null-rate checks, price-outlier detection, and sample routes before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines invest heavily in scraping detection. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
Airline booking engines use aggressive bot mitigation. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to bypass WAF blocks.
AirAsia X requires valid session tokens to progress from flight search to ancillary pricing. We maintain stateful Playwright sessions that navigate the booking flow exactly like a human user.
Booking interfaces change frequently. Our selector strategy uses multiple fallback chains per field - CSS selectors, XPath, and API interception - so a frontend update does not break your data feed.
Airlines cache search results heavily. We use cache-busting techniques and specific query parameters to ensure we extract live, up-to-the-minute inventory and pricing.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, fare outliers, schema drift, and coverage drops - and respond before you notice.
Rival airlines and OTAs monitor AirAsia X pricing on overlapping routes to adjust their own revenue management algorithms.
Revenue analysts track competitor load factors and fare bucket availability to optimise their own pricing curves.
Travel aggregators pull direct schedules and fares to build comprehensive meta-search engines without relying on expensive GDS fees.
Machine learning teams use historical pricing datasets to train models that predict future fare movements for consumer apps.
Aviation consultants analyse frequency, aircraft deployment, and base fares to model network profitability.
Industry analysts track changes in baggage and seat selection fees to understand low-cost carrier revenue strategies.
"AirAsia X operates one of the most dynamic pricing models in aviation, but extracting those fare signals requires bypassing aggressive bot mitigation."
Most teams underestimate the investment required: reliable airline scraping requires residential proxies, session token management, CAPTCHA handling, 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 airasia.x 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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows for the booking engine.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions where required to maintain search context.
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 airasia.x scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing is generally permissible under applicable law. DataFlirt targets only public, non-authenticated route and fare data. We do not extract personal passenger data or bypass authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to bypass WAFs and bot mitigation systems used by airlines.
Yes. We navigate the booking flow to extract dynamic pricing for checked baggage, seat selection, and in-flight meals, which are often hidden behind JavaScript interactions.
Real-time streaming pipelines achieve low latency for specific route monitoring. Full network refreshes run at daily cadences depending on the scale of origin-destination pairs requested.
Yes. We can configure the pipeline to extract direct flights, Fly-Thru transit options, and complex multi-leg itineraries.
Absolutely. We provide a sample run of specific routes 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 a one-off route dump or a continuous fare-monitoring feed across 10,000 routes - we scope, build, and operate the pipeline. Tell us what you need.
