We extract route schedules, dynamic fare pricing, seat availability, and fleet data directly from ana.co.jp. 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 ana.co.jp. All fields typed and schema-versioned.
"flight_number": "NH110", "departure_airport": "HND", "arrival_airport": "JFK", "departure_time": "2026-05-12T10:20:00Z", "arrival_time": "2026-05-12T10:15:00Z", "aircraft_type": "Boeing 777-300ER", "duration_minutes": 775, "stops": 0, "operating_carrier": "ANA"
| # | flight_number | departure_airport | arrival_airport | departure_time | arrival_time | aircraft_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from ana.co.jp. All fields typed and schema-versioned.
"flight_number": "NH110", "departure_date": "2026-05-12", "cabin_class": "Economy", "fare_type": "Super Value", "total_price": 145000.0, "currency": "JPY", "seats_remaining": 4, "refundable": false
| # | flight_number | departure_date | cabin_class | fare_type | base_price | taxes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Data objects from ana.co.jp. All fields typed and schema-versioned.
"origin": "HND", "destination": "JFK", "distance_miles": 6772, "frequency_weekly": 14, "operating_carrier": "ANA", "seasonal_schedule": false, "region": "North America"
| # | origin | destination | distance_miles | frequency_weekly | operating_carrier | seasonal_schedule |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seat Availability objects from ana.co.jp. All fields typed and schema-versioned.
"flight_number": "NH110", "departure_date": "2026-05-12", "cabin_class": "Business", "total_seats": 68, "available_seats": 12, "booked_seats": 56, "equipment_code": "77W", "last_updated": "2026-05-10T08:14:00Z"
| # | flight_number | departure_date | cabin_class | total_seats | available_seats | booked_seats |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Aircraft & Fleet objects from ana.co.jp. All fields typed and schema-versioned.
"aircraft_code": "77W", "manufacturer": "Boeing", "model": "777-300ER", "total_capacity": 212, "first_class_seats": 8, "business_class_seats": 68, "premium_economy_seats": 24, "economy_seats": 112, "wifi_available": true
| # | aircraft_code | manufacturer | model | total_capacity | first_class_seats | business_class_seats |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our ANA scraper navigates complex booking flows, multi-city itineraries, and dynamic fare matrices with full JavaScript rendering and session state management.
Departure times, arrival times, flight durations, aircraft types, and layover details for all domestic and international routes.
Capture pricing across all fare tiers including Super Value, Flex, and Premium Class fares with full tax breakdowns.
Extract remaining seat counts and cabin class capacity indicators directly from the booking engine.
Normalise pricing data across JPY, USD, EUR, and other regional currencies supported by the ANA portal.
Unified schema covering both Japan domestic flights and long-haul international route networks.
Identify operating carriers versus marketing carriers for Star Alliance and joint venture flights.
Extract checked baggage allowances, excess weight fees, and optional service charges per fare class.
Monitor equipment swaps and cabin configurations assigned to specific flights up to departure.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, or flight numbers. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for ana.co.jp.
Schema validation, null-rate checks, price-outlier detection, and sample schedules before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airline booking engines invest heavily in scraping detection. Here is how we stay resilient.
Airline portals use strict WAF rules and rate limiting. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management trained on real user behaviour patterns.
ANA search results are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution, lazy-load triggering, and dynamic price widget hydration.
Extracting fares requires maintaining session state across multi-step search forms. We handle the token exchanges and cookie persistence required to reach the final pricing matrix.
Booking engines change DOM structure frequently. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline overnight.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops.
Airlines and OTAs monitor ANA pricing matrices to adjust their own yield management systems and maintain parity.
Analysts track frequency changes, equipment swaps, and seat availability to model route performance and market share.
Travel agencies sync schedule and fare data to ensure their internal caching systems reflect accurate availability.
Metasearch engines populate their flight comparison results with direct structured data from the carrier.
Enterprise procurement teams audit booked fares against public availability to enforce travel policy compliance.
Revenue management teams correlate booking velocity and fare class availability with macroeconomic indicators.
"ANA operates one of the most complex pricing matrices in Asian aviation, making schedule and fare extraction critical for market parity."
Aviation data extraction requires navigating strict rate limits, complex multi-step booking engines, and aggressive bot mitigation. DataFlirt manages the residential proxies, session state, and JavaScript execution required to extract ANA fares at scale, allowing your analysts to focus on yield management rather than infrastructure.
Everything supported by our ana.co.jp 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.
We maintain pools of residential ISP proxies across JP and global regions. Rotation happens per-session to maintain state through booking flows.
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 ana.co.jp 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 GDPR.
We use persistent cookie sessions bound to specific residential IPs to navigate the multi-step search flow, ensuring the pricing matrix loads correctly before extraction.
Yes. We extract the full grid of available fare classes for any given flight, including promotional tiers, standard economy, and premium cabin pricing.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined set of origin-destination pairs.
Yes. Our schema normalises data across both Japan domestic flight networks and long-haul international routes operated by ANA.
Absolutely. We provide a sample run of up to 50 routes 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 route schedule dump or a continuous price-monitoring feed across thousands of flights, we scope, build, and operate the pipeline. Tell us what you need.
