We extract flight schedules, dynamic pricing, seat availability, fare tiers, and SKYPASS mileage requirements from Korean Air. 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 koreanair.com. All fields typed and schema-versioned.
"flight_number": "KE082", "origin_airport": "JFK", "destination_airport": "ICN", "departure_time": "2026-10-14T14:00:00Z", "arrival_time": "2026-10-15T17:20:00Z", "duration_minutes": 920, "aircraft_type": "Boeing 777-300ER", "stops": 0
| # | flight_number | origin_airport | destination_airport | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Fares objects from koreanair.com. All fields typed and schema-versioned.
"flight_number": "KE082", "cabin_class": "Prestige", "total_price": 4250.0, "currency": "USD", "base_fare": 3800.0, "taxes": 150.0, "fuel_surcharge": 300.0, "refundable": true
| # | flight_number | cabin_class | total_price | currency | base_fare | taxes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seat Availability objects from koreanair.com. All fields typed and schema-versioned.
"flight_number": "KE082", "departure_date": "2026-10-14", "cabin_class": "Economy", "seats_available": 4, "booking_class_code": "Y", "seat_pitch_inches": 33, "cabin_layout": "3-3-3", "power_available": true
| # | flight_number | departure_date | cabin_class | seats_available | booking_class_code | seat_pitch_inches |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for SKYPASS Awards objects from koreanair.com. All fields typed and schema-versioned.
"origin_airport": "JFK", "destination_airport": "ICN", "cabin_class": "Prestige", "miles_required": 62500, "cash_surcharge": 215.5, "peak_season_flag": false, "award_availability": true, "upgrade_eligible": true
| # | origin_airport | destination_airport | cabin_class | miles_required | cash_surcharge | peak_season_flag |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Fleet & Amenities objects from koreanair.com. All fields typed and schema-versioned.
"aircraft_code": "77W", "aircraft_model": "Boeing 777-300ER", "wifi_available": true, "in_flight_entertainment": "AVOD", "meal_service": "Full Meal", "lounge_access_eligible": true, "total_capacity": 277
| # | aircraft_code | aircraft_model | wifi_available | in_flight_entertainment | meal_service | lounge_access_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Korean Air scraper handles the multi-step search flows, session persistence, and IP routing required to extract accurate fares, schedules, and award availability.
Extract origin, destination, layovers, flight duration, and aircraft models for the entire Korean Air network.
Capture base fares, taxes, and fuel surcharges across multiple currencies and points of sale.
Track pricing and availability across First, Prestige, and Economy classes with specific booking codes.
Monitor mileage requirements, peak season blackout dates, and cash surcharges for award travel.
Separate base fare from government taxes and carrier imposed surcharges for accurate yield analysis.
Determine actual passenger load factors by parsing available and occupied seats on the seat map.
Route requests through specific regional proxies to capture point-of-sale pricing differences.
Extract baggage allowances, cancellation penalties, and change fees associated with specific fare buckets.
Configure pipelines to poll specific routes at hourly or daily intervals to monitor fare fluctuations.
Brief in. Clean data out.
Provide route pairs, dates, and cabin classes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for koreanair.com.
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.
Airline booking engines use strict rate limits and session validation. Here is how we maintain data integrity.
Airline pricing requires sequential API calls mimicking a user journey. We maintain strict session cookies across the search, flight selection, and pricing steps to ensure the final fare matches the initial query.
A flight from ICN to JFK prices differently when searched from a US IP versus a Korean IP. We route requests through targeted residential proxies to capture accurate point-of-sale pricing.
Airline booking engines aggressively block high-velocity searches. We distribute requests across thousands of IPs and introduce randomised delays to stay below bot detection thresholds.
Korean Air loads pricing data asynchronously via XHR. We use Playwright to execute the JavaScript payloads and intercept the raw JSON responses directly from the network layer.
We normalise all departure and arrival times to UTC while preserving local time strings, and standardise currency outputs to match your internal accounting requirements.
Rival airlines track Korean Air pricing on overlapping routes to adjust their own dynamic pricing models.
OTAs and meta-search engines ingest direct pricing to supplement GDS data and verify fare accuracy.
Frequent flyer services monitor SKYPASS availability to alert users when Prestige or First class award seats open.
Analysts track seat map availability over time to estimate passenger load factors and revenue per available seat mile.
Aviation consultants monitor schedule changes, aircraft downgrades, and frequency adjustments to map capacity trends.
Enterprise procurement teams audit booked fares against public pricing to ensure travel management companies deliver negotiated rates.
"Airline pricing is highly dynamic and geo-dependent. Capturing accurate fare data from Korean Air requires managing session state and regional IP routing precisely."
Most teams fail at airline scraping because they ignore session cookies and geo-fenced pricing. DataFlirt manages the complex multi-step search flows, session persistence, and IP routing required to extract accurate Korean Air fares, so your engineers can focus on yield analysis rather than infrastructure maintenance.
Everything supported by our koreanair.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 route orchestration and retry logic. Playwright manages the complex multi-step search flows and intercepts XHR pricing payloads directly from the network.
We maintain proxy pools across key global markets to capture accurate point-of-sale pricing. Sticky sessions ensure the IP remains constant throughout the search flow.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling for high-frequency route polling. All state and historical pricing stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About koreanair.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal passenger information (PNR data) or breach authenticated SKYPASS accounts. Clients should review airline ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, strict session management, and request timing modelled on human behaviour. We intercept XHR payloads rather than parsing HTML to reduce load on the target servers and improve extraction reliability.
Yes. We route requests through regional proxy pools (e.g., South Korea, US, Europe) to capture geo-fenced pricing differences accurately.
We support polling frequencies down to 15-minute intervals for high-priority routes, subject to rate limit management across our proxy pools.
Yes. We track mileage requirements, cash surcharges, and seat availability for award bookings across all cabin classes.
Our minimum engagement starts at tracking 500 route pairs with daily delivery. Contact us with your specific route list and frequency requirements for a scoped quote.
Yes. We provide a sample run of up to 20 route pairs as part of the pre-engagement scoping process to validate schema fit and data accuracy.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily schedule export or continuous fare monitoring across 10,000 routes, we scope, build, and operate the pipeline. Tell us what you need.
