We extract flight schedules, dynamic pricing, cabin availability, and route metadata from Air France. 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 airfrance.com. All fields typed and schema-versioned.
"flight_number": "AF006", "origin": "CDG", "destination": "JFK", "departure_time": "2026-08-14T14:30:00Z", "arrival_time": "2026-08-14T16:45:00Z", "duration": "495", "aircraft_type": "Boeing 777-300ER"
| # | flight_number | origin | destination | departure_time | arrival_time | duration |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from airfrance.com. All fields typed and schema-versioned.
"flight_number": "AF006", "currency": "EUR", "base_fare": 450.0, "taxes": 124.5, "total_price": 574.5, "cabin_class": "Economy", "fare_type": "Standard"
| # | flight_number | currency | base_fare | taxes | total_price | cabin_class |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Cabin Availability objects from airfrance.com. All fields typed and schema-versioned.
"flight_number": "AF006", "date": "2026-08-14", "cabin_class": "Business", "seats_remaining": 4, "waitlist_status": false, "booking_code": "J", "fare_basis": "J12345"
| # | flight_number | date | cabin_class | seats_remaining | waitlist_status | booking_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ancillary Services objects from airfrance.com. All fields typed and schema-versioned.
"flight_number": "AF006", "baggage_allowance": "1x23kg", "extra_bag_fee": 75.0, "seat_selection_fee": 20.0, "wifi_available": true, "meal_included": true, "lounge_access": false
| # | flight_number | baggage_allowance | extra_bag_fee | seat_selection_fee | wifi_available | meal_included |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Intelligence objects from airfrance.com. All fields typed and schema-versioned.
"origin_airport": "CDG", "destination_airport": "JFK", "distance": 5836, "frequency_weekly": 35, "seasonal_route": false, "first_flight_date": "2026-01-01", "competitor_airlines": "['DL', 'AA']"
| # | origin_airport | destination_airport | distance | frequency_weekly | seasonal_route | first_flight_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Air France scraper handles every layer of the platform: flight schedules, dynamic pricing, cabin availability, and route intelligence with JavaScript rendering and anti-bot circumvention built in.
Flight numbers, departure times, arrival times, aircraft types, and operating carriers scraped across all routes.
Capture base fares, taxes, total prices, and fare types across all cabin classes timestamped per crawl.
Extract seat counts, booking codes, and waitlist status for La Première, Business, Premium Economy, and Economy.
Track reward flight availability and mileage requirements alongside cash prices for loyalty program analysis.
Baggage allowances, seat selection fees, and lounge access costs mapped to specific fare buckets.
Simulate searches from different geographic regions to capture point-of-sale pricing variations.
Identify true operating carriers for flights marketed by Air France but operated by KLM, Delta, or other SkyTeam partners.
Extract flexible date pricing matrices to build comprehensive fare curves and identify cheapest travel days.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences.
Brief in. Clean data out.
Provide route lists, date ranges, cabin classes, or point-of-sale requirements. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for airfrance.com.
Schema validation, null-rate checks, price-outlier detection, and schedule 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. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Air France uses advanced bot detection to block automated flight searches. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management trained on real user behaviour.
The Air France booking engine is a complex single-page application. We run full Playwright browser sessions with JavaScript execution, capturing dynamic pricing and availability that headless HTTP clients miss entirely.
Flight searches require maintaining complex session states across multiple API calls. Our infrastructure handles token negotiation and cookie persistence to ensure searches complete successfully without triggering security blocks.
For large route networks, we maintain a hash index of last-seen values per flight. Subsequent runs only push diffs, reducing compute cost and downstream processing load. You get a clean changelog rather than full re-dumps.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops. SLA uptime is contractual, not aspirational.
Airlines and OTAs monitor Air France pricing on key routes to adjust their own revenue management algorithms.
Enterprise travel managers track historical fare data to negotiate better corporate rates and optimise booking windows.
Aviation analysts track route frequencies, aircraft deployment, and network expansion to identify market trends.
Reward travel platforms extract Flying Blue mileage requirements to help users find the best redemption values.
Revenue management teams correlate seat availability and price changes to model passenger demand curves.
Machine learning teams use historical flight schedules and pricing datasets to train predictive pricing models.
"Air France operates one of the most dynamic pricing engines in European aviation, but none of it is queryable unless you build the pipeline."
Most teams underestimate the investment required: reliable airline scraping requires residential proxies, full JavaScript rendering, 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 airfrance.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 for the booking engine.
We maintain pools of residential ISP proxies across European regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda for burst and ECS for sustained loads. 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 airfrance.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Air France is generally permissible under applicable law. DataFlirt targets only public, non-authenticated flight schedules, pricing, and availability data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review terms of service and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes do not break the pipeline. We monitor for rate spikes in real time and trigger pool rotation automatically.
Yes. We can configure pipelines to extract flexible date matrices, allowing you to build comprehensive fare curves and track price evolution as departure dates approach.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined route set. Full network refreshes complete within agreed daily windows.
Our smallest packages start at a defined route list with daily delivery. For larger networks or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
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, 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 one-off schedule dump or a continuous price-monitoring feed across thousands of routes, we scope, build, and operate the pipeline. Tell us what you need.
