We extract flight schedules, dynamic pricing, seat availability, and route intelligence from jal.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 Schedules objects from jal.com. All fields typed and schema-versioned.
"flight_number": "JL005", "origin_airport": "JFK", "destination_airport": "HND", "departure_time": "2026-10-14T13:30:00Z", "arrival_time": "2026-10-15T16:35:00Z", "duration_minutes": 845, "aircraft_type": "Airbus A350-1000", "operated_by": "Japan Airlines"
| # | flight_number | origin_airport | destination_airport | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from jal.com. All fields typed and schema-versioned.
"flight_number": "JL005", "departure_date": "2026-10-14", "cabin_class": "Business", "fare_type": "Semi-Flex", "base_fare": 4200.0, "taxes_fees": 345.5, "total_price": 4545.5, "currency": "USD"
| # | flight_number | departure_date | cabin_class | fare_type | base_fare | taxes_fees |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seat Availability objects from jal.com. All fields typed and schema-versioned.
"flight_number": "JL005", "departure_date": "2026-10-14", "cabin_class": "Business", "total_seats": 54, "available_seats": 12, "blocked_seats": 42, "pitch_inches": 74.0, "width_inches": 20.5
| # | flight_number | departure_date | cabin_class | total_seats | available_seats | blocked_seats |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Intelligence objects from jal.com. All fields typed and schema-versioned.
"origin_iata": "JFK", "destination_iata": "HND", "distance_miles": 6772, "frequency_weekly": 14, "direct_flights": true, "codeshare_partners": "['American Airlines', 'JetBlue']", "season_validity_start": "2026-03-29"
| # | origin_iata | destination_iata | distance_miles | frequency_weekly | direct_flights | connecting_hubs |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ancillary Data objects from jal.com. All fields typed and schema-versioned.
"flight_number": "JL005", "cabin_class": "Business", "baggage_allowance_pcs": 3, "baggage_allowance_kg": 32, "wifi_available": true, "lounge_access": "['Sakura Lounge', 'Oneworld First Class Lounge']", "change_fee": 0.0, "cancellation_fee": 250.0
| # | flight_number | cabin_class | baggage_allowance_pcs | baggage_allowance_kg | extra_bag_fee | wifi_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Airline booking engines enforce strict session states and anti-bot perimeters. Our jal.com scraper handles multi-step search flows, session cookies, and dynamic hydration to deliver accurate fare grids.
Extract comprehensive flight schedules across the entire JAL network, including departure/arrival times, aircraft types, and codeshare designations.
Capture base fares, taxes, and total prices across Economy, Premium Economy, Business, and First Class cabins for any date range.
Monitor remaining seat counts, fare bucket availability, and cabin capacity thresholds to map supply against pricing.
Execute complex itinerary searches including domestic Japanese connections and international long-haul layovers.
Extract detailed baggage allowances, cancellation penalties, change fees, and JMB mileage accrual rates per fare class.
Query fares across different points-of-sale (POS) to capture regional pricing variations and currency conversions.
Track fare volatility with intra-day polling schedules, capturing yield management adjustments in near real-time.
Track aircraft assignments, seat map configurations, and operational changes across the schedule.
Minimise payload size by emitting only schedule changes, aircraft swaps, or fare updates since the last extraction run.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and cabin classes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for jal.com.
Schema validation, null-rate checks, price-outlier detection, and sample itineraries before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airline websites are notorious for session timeouts and aggressive bot mitigation. Here is how we maintain stable extraction at scale.
Flight searches on jal.com require multi-step flows where session tokens are passed between the initial search form and the booking engine backend. Our Playwright orchestrator maintains strict cookie jars and session states to prevent timeout errors and invalid request blocks.
High-frequency fare polling triggers immediate IP bans. We distribute requests across a global pool of residential ISP proxies, matching the point-of-sale region to the IP address while spoofing TLS fingerprints to mimic legitimate traveller behaviour.
Airline fare grids are heavily nested and dynamically hydrated via JavaScript. We map the exact DOM structure of the pricing matrix, using resilient XPath and CSS selectors to extract base fares, taxes, and cabin classes accurately.
To ensure pipeline stability, we implement intelligent pacing. Requests are queued and executed with randomised delays and concurrency limits that respect the target server's capacity, preventing 429 Too Many Requests errors.
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. SLA uptime is contractual, not aspirational.
Rival airlines and OTAs track JAL's pricing strategy on overlapping routes to adjust their own yield management models.
Travel aggregators ingest direct jal.com schedules and fares to ensure parity with GDS feeds and display accurate inventory.
Aviation analysts track frequency, aircraft deployment, and pricing tiers to estimate route yields and network profitability.
Data science teams use historical fare volatility datasets to train predictive pricing algorithms and demand forecasting models.
Corporate risk platforms monitor schedule changes, cancellations, and aircraft swaps to alert travellers of potential disruptions.
Procurement teams audit public fares against negotiated corporate rates to ensure contract compliance and calculate savings.
"Airline pricing is the original dynamic market — capturing JAL's fare volatility requires infrastructure that respects strict session states and anti-bot perimeters."
Most teams fail at airline scraping because booking engines enforce strict session timeouts and behavioural checks. DataFlirt manages the proxy rotation, JavaScript execution, and session state required to extract accurate pricing and availability without triggering rate limits. We handle the infrastructure; you consume the structured data.
Everything supported by our jal.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 jal.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available schedule and pricing information is generally permissible. DataFlirt targets only public, non-authenticated flight data. We do not extract personal passenger data, circumvent authentication walls, or violate data privacy regulations. Clients should review JAL's ToS and consult legal counsel for specific use cases.
Our Playwright orchestrator maintains strict cookie jars and session states throughout the multi-step search flow. We simulate human interaction speeds and manage the required tokens passed between the frontend and the booking backend to prevent timeout errors.
Yes. We can configure the pipeline to query fares using different point-of-sale (POS) parameters, allowing extraction of pricing in JPY, USD, EUR, or other supported regional currencies.
We can configure pipelines for intra-day polling (e.g., every 4, 6, or 12 hours) depending on your target route volume. High-frequency polling requires larger proxy pools to distribute requests and avoid rate limits.
Yes. The pipeline can execute multi-city searches, capturing the full breakdown of connecting flights, layover durations, and combined pricing across the entire journey.
Absolutely. We provide a sample run of up to 100 route permutations as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily schedule snapshot or continuous fare monitoring across 10,000 routes — we scope, build, and operate the pipeline. Tell us what you need.
