We extract flight schedules, dynamic pricing, fare classes, routing options, and availability from lufthansa.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 lufthansa.com. All fields typed and schema-versioned.
"flight_number": "LH400", "origin": "FRA", "destination": "JFK", "departure_time": "2024-10-12T10:50:00Z", "arrival_time": "2024-10-12T13:40:00Z", "duration": "8h 50m", "aircraft_type": "Boeing 747-8", "operating_airline": "Lufthansa"
| # | flight_number | origin | destination | departure_time | arrival_time | duration |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from lufthansa.com. All fields typed and schema-versioned.
"flight_number": "LH400", "cabin_class": "Economy", "fare_type": "Economy Classic", "total_price": 492.5, "currency": "EUR", "miles_required": 25000, "booking_class": "K", "price_timestamp": "2024-05-12T09:14:00Z"
| # | flight_number | cabin_class | fare_type | base_fare | taxes | total_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Routing & Layovers objects from lufthansa.com. All fields typed and schema-versioned.
"itinerary_id": "FRA-JFK-LH400", "origin": "FRA", "destination": "JFK", "total_duration": "8h 50m", "segment_count": 1, "layover_airports": "[]", "layover_durations": "[]", "total_price": 492.5
| # | itinerary_id | origin | destination | total_duration | segment_count | segments |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Availability & Seats objects from lufthansa.com. All fields typed and schema-versioned.
"flight_number": "LH400", "date": "2024-10-12", "cabin_class": "Business", "seats_remaining": 4, "wifi_available": true, "pitch": "78 inch", "width": "20 inch", "power_outlets": true
| # | flight_number | date | cabin_class | seats_remaining | waitlist_available | seat_map_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Baggage & Ancillaries objects from lufthansa.com. All fields typed and schema-versioned.
"fare_type": "Economy Light", "cabin_baggage_allowance": "1 x 8kg", "checked_baggage_allowance": "0", "extra_bag_fee": 65.0, "seat_selection_fee": 25.0, "refund_policy": "Non-refundable", "change_fee": 150.0, "lounge_access": false
| # | fare_type | cabin_baggage_allowance | checked_baggage_allowance | extra_bag_fee | seat_selection_fee | refund_policy |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Lufthansa scraper handles complex booking flows, multi-leg routing, dynamic pricing, and fare class variations with full session management and anti-bot circumvention built in.
Extract direct flights, multi-city itineraries, codeshares, and layover durations across the entire Lufthansa network.
Capture real-time pricing across Economy Light, Classic, Flex, Business, and First Class tiers.
Track remaining seats per cabin class to model demand and pricing algorithms.
Extract checked baggage allowances, seat selection fees, and cancellation policies per fare type.
Capture award flight availability and point requirements alongside cash prices.
Rotate IP origins to capture point-of-sale specific pricing and currency conversions.
Run scheduled extractions at hourly or daily cadences to track fare volatility leading up to departure.
Extract equipment types, seat pitch, Wi-Fi availability, and in-flight service indicators.
Maintain hash indexes of last-seen fares and only push diffs to reduce downstream processing load.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and required fare classes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for lufthansa.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.
Airlines invest heavily in scraping detection to protect pricing data. Here is how we stay resilient.
Lufthansa uses advanced bot mitigation. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full TLS spoofing.
Flight searches require multi-step stateful interactions. We maintain persistent cookie sessions and execute JavaScript flows exactly like a human user.
Airlines change prices based on user geography. We route requests through region-specific residential proxies to capture the exact local fare.
Lufthansa frequently updates its booking interface. Our selector strategy uses multiple fallback chains so layout changes do not break your data pipeline.
Every run emits structured logs. We alert on null-rate spikes, fare outliers, and coverage drops. SLA uptime is contractual.
Rival airlines and OTAs track Lufthansa fares across key routes to adjust their own dynamic pricing algorithms.
Enterprise procurement teams monitor historical fare trends to negotiate better corporate rates and optimise booking windows.
Analysts correlate seat availability drops and fare increases to model passenger demand on specific European and transatlantic routes.
Aviation consultants track capacity, frequency changes, and route expansion to evaluate market share and network strategy.
Frequent flyer platforms aggregate Miles & More availability to alert users when premium cabin award seats open up.
Hedge funds and PE firms extract real-time booking velocity signals to forecast quarterly revenue performance.
"Airlines treat pricing data as highly confidential IP. Extracting it at scale requires bypassing military-grade bot protection and complex stateful booking flows."
Most teams underestimate the investment required: reliable Lufthansa 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 lufthansa.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. Playwright handles JavaScript rendering, cookie sessions, and multi-step booking flows.
We maintain pools of residential ISP proxies across European regions to ensure accurate point-of-sale pricing and bypass IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About lufthansa.com 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 data. We do not extract personal passenger data or circumvent authentication walls. Clients should review Terms of Service and consult legal counsel.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for CAPTCHA rate spikes and trigger solver queues automatically.
Yes. We route requests through region-specific proxies to simulate users searching from specific countries, capturing the exact local fare and currency.
Real-time streaming pipelines achieve sub-60-minute latency for pricing on defined routes. Full schedule refreshes at daily cadence complete within a 6-12 hour window.
Yes. We monitor remaining seat indicators across different cabin classes to help model demand and capacity.
Our smallest packages start at a defined route list (typically 1,000-10,000 routes) with weekly delivery. For larger networks, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 100 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 schedule dump or a continuous price-monitoring feed across 5,000 routes - we scope, build, and operate the pipeline. Tell us what you need.
