Extract live and historical flight fares, schedules, seat availability, ancillary pricing, and route data from 500+ airlines and all major OTAs. The data backbone for travel tech platforms, revenue management systems, and flight price intelligence products.
Aviation data scraping is the automated collection of structured flight information from airline websites, online travel agencies (OTAs), global distribution systems (GDS), and flight comparison platforms. A single route query surface an enormous amount of structured data: itinerary options across multiple airlines, fares broken down by base price and tax, seat availability at the fare class level, baggage allowances, refund conditions, codeshare relationships, and ancillary product pricing for seat selection, meals, and priority boarding. Scraping this data programmatically β across hundreds of routes and booking windows simultaneously β gives travel businesses the market intelligence they need to compete effectively.
Airline pricing is one of the most complex dynamic pricing problems in any industry. Fares change thousands of times a day per route, driven by demand signals, competitor pricing, inventory management rules, and revenue optimisation algorithms. The gap between the best and worst fare on a given route at a given moment can be enormous β and it collapses or widens within minutes. For revenue managers, travel tech developers, and fare intelligence platforms, having a continuous, accurate feed of this data is not a nice-to-have: it is the product.
DataFlirt's aviation scraping infrastructure is built for this environment. We handle the significant technical challenges that airline and OTA sites present: JavaScript-rendered booking engines with multi-step search flows, CAPTCHA systems, session management, bot detection based on search pattern analysis, and geo-restricted pricing that differs by the apparent location of the searcher. Our infrastructure simulates authentic booking sessions across multiple origin countries to retrieve geo-accurate fare data.
Beyond point-in-time fare queries, we provide historical fare archives β critical for revenue management modelling, booking curve analysis, and seasonality research. We also collect schedule data, including timetable information, codeshare arrangements, aircraft types, and on-time performance signals. Whether you need a live fare feed for a travel metasearch engine or a deep historical dataset for yield management model training, DataFlirt's aviation data infrastructure covers both.
Comprehensive extraction built for reliability, accuracy, and scale.
Continuous fare collection across cabin classes, booking windows, and routing options β capturing every price point across airline direct and OTA channels simultaneously.
Extract departure and arrival times, codeshare flights, alliance memberships, layover details, aircraft type, and schedule change notifications.
Monitor seat availability at the fare class level β revealing not just whether seats exist, but how many remain in each booking class.
Capture baggage fee structures, seat selection pricing, meal options, priority boarding, lounge access, and in-flight upgrade pricing from each carrier.
Access to deep historical fare records for route-level trend analysis, booking curve modelling, and seasonal pricing pattern research.
Aggregate fares from Expedia, Kayak, Google Flights, MakeMyTrip, Cleartrip, Booking.com, Skyscanner, and 50+ more into a single normalised dataset.
Every field you need, structured and ready to use downstream.
A proven process that turns any source into clean structured data β reliably.
{ "status": "success", "source": "ota_aggregated", "queried_at": "2025-03-18T11:42:00Z", "route": { "origin": "BOM", "destination": "LHR", "depart_date": "2025-04-10", "cabin": "economy" }, "itineraries": [ { "airline": "Air India", "flight": "AI 131", "departs": "02:10", "arrives": "07:25+1", "stops": 0, "duration_min": 555, "base_fare": 38200, "total_price": 51490, "currency": "INR", "seats_left": 4, "fare_class": "V", "baggage_kg": 23, "refundable": false } ], "sources_checked": ["airline_direct","expedia","kayak"] }
Built on proven open-source tools and cloud infrastructure β no vendor lock-in.
Country-specific residential proxies simulate authentic search sessions from each target market for geo-accurate fare retrieval.
Playwright automates complex multi-page search flows on airline and OTA sites that cannot be accessed via simple HTTP requests.
Distributed workers query hundreds of routes simultaneously, maintaining near real-time fare coverage across your defined network.
Intelligent diff engine flags price changes, class closures, and new inventory openings with timestamps for change-driven alerting.
Automated forward-looking queries across multiple booking windows capture how fares evolve from 365 days out to day-of-departure.
All fares normalised to a consistent schema regardless of source β IATA codes, standardised cabin classes, and unified currency conversion.
From solo analysts to enterprise data teams β here's how organizations use this data.
Airline fares change thousands of times daily per route, driven by inventory rules, demand signals, and competitor moves that interact in real time. Getting this data reliably β at scale, with geo-accuracy, across both airline direct and OTA channels β requires infrastructure purpose-built for the aviation environment. DataFlirt delivers structured, continuously updated flight data that travel tech companies, revenue managers, and intelligence platforms use to compete on price and insight.
Start free and scale as your data needs grow.
For small teams and projects getting started with data.
For growing teams with serious data requirements.
For large organizations with custom requirements.
Everything you need to know before getting started.
Join data teams worldwide using DataFlirt to power products, research, and operations with reliable, structured web data.
