We extract flight schedules, dynamic fare pricing, seat availability, and Frequent Flyer point redemptions from Qantas. 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 qantas.com. All fields typed and schema-versioned.
"flight_number": "QF1", "origin_iata": "SYD", "destination_iata": "LHR", "departure_time_local": "2026-08-14T15:55:00", "arrival_time_local": "2026-08-15T06:35:00", "aircraft_type": "Airbus A380-800", "duration_minutes": 1360, "operating_carrier": "Qantas"
| # | flight_number | origin_iata | destination_iata | departure_time_local | arrival_time_local | aircraft_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fare Pricing objects from qantas.com. All fields typed and schema-versioned.
"flight_number": "QF1", "cabin_class": "Economy", "fare_family": "Red e-Deal", "price_total": 1845.0, "currency": "AUD", "seats_remaining": 4, "booking_class_code": "O", "scrape_timestamp": "2026-05-12T10:14:00Z"
| # | flight_number | cabin_class | fare_family | price_total | currency | tax_amount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Frequent Flyer Rewards objects from qantas.com. All fields typed and schema-versioned.
"flight_number": "QF93", "origin": "MEL", "destination": "LAX", "reward_type": "Classic Flight Reward", "points_required": 41900, "taxes_fees_amount": 184.5, "taxes_fees_currency": "AUD", "cabin_class": "Economy", "availability_status": "Available"
| # | flight_number | origin | destination | reward_type | points_required | taxes_fees_amount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from qantas.com. All fields typed and schema-versioned.
"origin_code": "SYD", "destination_code": "HND", "distance_km": 7820, "direct_flight": true, "frequency_per_week": 14, "seasonal_route": false, "alliance_partners": "['Japan Airlines']", "route_active": true
| # | origin_code | destination_code | distance_km | direct_flight | frequency_per_week | seasonal_route |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Aircraft & Amenities objects from qantas.com. All fields typed and schema-versioned.
"aircraft_code": "388", "aircraft_name": "Airbus A380-800", "wifi_available": true, "seat_pitch_inches": 31, "in_seat_power": true, "entertainment_type": "Seatback Screen", "meal_service_included": true, "total_capacity": 485
| # | aircraft_code | aircraft_name | wifi_available | seat_pitch_inches | in_seat_power | entertainment_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Qantas scraper handles every layer of the booking platform: schedule grids, dynamic fare buckets, Classic Reward availability, and route networks - with JavaScript rendering, session state management, and anti-bot circumvention built in.
Origin, destination, departure and arrival times, aircraft type, duration, and codeshare operating carriers scraped across all routes.
Capture Red e-Deal, Flex, Business, and First class pricing buckets. Track base fares, taxes, and total costs timestamped per crawl.
Extract Classic Flight Rewards and Points Plus Pay pricing. Monitor point requirements and tax components for loyalty program analysis.
Capture remaining seat counters and booking class inventory codes to model load factors and demand curves.
Extract complex itinerary pricing, connection times, and layover details for multi-leg journeys.
Track baggage allowance tiers, seat selection costs, and carbon offset pricing associated with specific fare families.
Complete coverage of domestic Australia sectors and international long-haul routes operated by Qantas.
Identify and extract codeshare flights operated by Emirates, British Airways, Japan Airlines, and other partners.
Run one-off bulk schedule exports or configure continuous pipelines at hourly cadences for volatile fare monitoring.
Brief in. Clean data out.
Provide origin-destination pairs, travel date ranges, or cabin classes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and bot protection handling for qantas.com.
Schema validation, null-rate checks, fare outlier detection, and itinerary sample tests 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 and dynamic pricing obfuscation. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
Airlines use aggressive bot protection like Akamai and Datadome. Our crawlers use AU-based residential ISP proxies with realistic browser fingerprints, randomised request timing, and full TLS session management.
Flight searches require maintaining session tokens across multiple API calls and redirects. We build resilient state machines that persist cookies and authorization headers throughout the booking flow.
Qantas fare calendars and dynamic pricing matrices are heavily JavaScript-rendered. We run full Playwright browser sessions to trigger lazy-loads and hydrate fare data that headless HTTP clients miss entirely.
For large route networks, we maintain a hash index of last-seen fares per flight. Subsequent runs only push diffs - reducing compute cost, storage bloat, and downstream processing load.
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.
Rival airlines and OTAs monitor Qantas fare buckets and promotional pricing to optimise their own revenue management systems.
Travel booking engines ingest schedule and pricing data to populate search results where direct API access is limited or cost-prohibitive.
Points aggregators track Classic Reward availability trends to notify frequent flyers when premium cabin seats are released.
Aviation analysts track frequency, capacity, and fare classes on domestic sectors to model yield and route profitability.
Travel Management Companies audit negotiated corporate rates against public fares to ensure policy compliance and cost savings.
Operations teams monitor schedule changes, aircraft swaps, and cancellations to predict network cascading effects.
"Qantas operates one of the most complex pricing engines in aviation. Extracting accurate fare buckets requires full session state and token management."
Most teams underestimate the investment required: reliable airline scraping requires residential proxies, full JavaScript rendering for calendar widgets, session token persistence, and strict anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our qantas.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 AU 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 qantas.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing from Qantas is generally permissible. DataFlirt targets only public, non-authenticated schedule and fare data. We do not extract personal data, circumvent authentication walls, or modify passenger records. Clients should review Qantas Terms of Use and consult legal counsel for specific use cases.
We use AU-based residential ISP proxies, full Playwright browser sessions with realistic TLS fingerprints, and request timing modelled on human behaviour. We monitor for block rate spikes in real time and trigger pool rotation automatically.
Yes. We track reward seat availability across cabins, including the points required and the associated taxes and carrier charges.
Real-time streaming pipelines achieve sub-60-minute latency for specific origin-destination pairs. Full network refreshes at daily cadence complete within a 6-12 hour window depending on route volume.
Yes. We extract the marketing carrier (Qantas) and the operating carrier (e.g., Emirates, Jetstar, British Airways) for all codeshare itineraries.
Our smallest packages start at a defined list of origin-destination pairs (typically 500-2,000 routes) with daily delivery. For larger global networks or high-frequency polling, we price based on volume and compute requirements.
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.
