We extract flight schedules, dynamic fare tiers, seat availability, and route metadata from Air New Zealand. 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 airnewzealand.com. All fields typed and schema-versioned.
"flight_number": "NZ2", "origin_code": "AKL", "destination_code": "JFK", "departure_time": "2026-08-14T19:40:00+12:00", "arrival_time": "2026-08-14T19:55:00-04:00", "duration_minutes": 975, "aircraft_type": "Boeing 787-9 Dreamliner", "direct_flight": true
| # | flight_number | origin_code | destination_code | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Fare Pricing objects from airnewzealand.com. All fields typed and schema-versioned.
"flight_number": "NZ101", "departure_date": "2026-09-10", "currency": "NZD", "seat_only_price": 249.0, "seat_bag_price": 279.0, "the_works_price": 299.0, "works_deluxe_price": 349.0, "taxes_fees": 45.5
| # | flight_number | departure_date | currency | seat_only_price | seat_bag_price | the_works_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Seat Availability objects from airnewzealand.com. All fields typed and schema-versioned.
"flight_number": "NZ2", "departure_date": "2026-08-14", "cabin_class": "Business Premier", "available_seats_count": 4, "pitch_inches": 79.5, "width_inches": 22.0, "power_outlets": true, "wifi_available": true
| # | flight_number | departure_date | cabin_class | available_seats_count | seat_map_url | pitch_inches |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Route Metadata objects from airnewzealand.com. All fields typed and schema-versioned.
"origin_code": "AKL", "destination_code": "LAX", "distance_km": 10487, "direct_flight": true, "layover_airport": "None", "days_of_operation": "['Mon', 'Wed', 'Fri', 'Sun']", "equipment_list": "['Boeing 777-300ER', 'Boeing 787-9']"
| # | origin_code | destination_code | distance_km | direct_flight | layover_airport | layover_duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Airpoints & Rewards objects from airnewzealand.com. All fields typed and schema-versioned.
"flight_number": "NZ101", "fare_class": "The Works", "airpoints_dollars_earned": 15, "status_points_earned": 30, "upgrade_eligible": true, "reward_seat_availability": true, "points_price": 299, "taxes_in_cash": 45.5
| # | flight_number | fare_class | airpoints_dollars_earned | status_points_earned | upgrade_eligible | reward_seat_availability |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Air New Zealand scraper handles the complexity of modern airline booking engines: session tokens, geo-fenced pricing, dynamic fare tiers, and strict anti-bot mitigation.
Extract origin, destination, departure and arrival times, aircraft types, and operating carrier details for any route pair.
Track Seat, Seat+Bag, The Works, Works Deluxe, Premium Economy, and Business Premier fares simultaneously.
Monitor remaining seat counts per cabin class and extract detailed seat map metadata including pitch and width.
Extract point-of-sale specific pricing using regional residential proxies to see fares exactly as local customers do.
Map direct flights versus layovers, including connection times and terminal changes for complex itineraries.
Capture Airpoints Dollars and Status Points earning rates specific to each flight and fare class combination.
Separate the base fare from government taxes, airport fees, and carrier-imposed surcharges.
Extract weight limits, piece concepts, and extra baggage costs associated with each fare tier.
Run daily route sweeps or configure high-frequency checks on specific dates to monitor real-time yield changes.
Brief in. Clean data out.
Provide origin/destination pairs, date ranges, and cabin classes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and anti-bot circumvention for airnewzealand.com.
Schema validation, null-rate checks, and fare-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines deploy aggressive bot mitigation to protect their inventory data. Here is how we maintain reliable extraction.
Airlines use strict Akamai or Datadome protection. Our crawlers use residential ISP proxies with realistic browser fingerprints and proper TLS handshakes to bypass enterprise-grade mitigation.
Flight searches require maintaining complex state, cookies, and session tokens across multiple requests. We manage full browser sessions to ensure the booking flow remains valid.
Airlines show different fares based on the IP address origin. We route requests through specific country nodes (e.g., NZ, AU, US) to capture accurate local pricing.
Modern airline booking engines are single-page applications. We use Playwright for full JavaScript execution, triggering dynamic fare loads and seat map hydration.
Every run emits structured logs. We alert on null fares, schema drift, and block rates, adjusting proxy pools and session logic automatically.
OTAs and competing airlines monitor fare changes across key trans-Tasman and Pacific routes to optimise their own pricing.
Metasearch engines ingest direct flight schedules and pricing to provide comprehensive comparison tools to consumers.
Aviation analysts track seat availability and fare bucket depletion over time to model demand and forecast revenue.
Enterprises monitor typical route costs and fare tiers to optimise travel budgets and negotiate corporate rates.
Consultants analyse frequency, capacity, and aircraft deployment to identify network expansion opportunities.
Points aggregators track Airpoints earning rates and reward seat availability to advise frequent flyers on optimal redemptions.
"Air New Zealand's booking engine holds the ground truth for trans-Tasman capacity, but extracting it reliably requires bypassing enterprise-grade bot mitigation."
Most teams underestimate the complexity of scraping airline infrastructure. Extracting fares from Air New Zealand requires managing strict session tokens, handling geo-targeted pricing variations, and bypassing aggressive anti-bot systems. DataFlirt absorbs that complexity so your engineers can focus on yield analysis and market intelligence, not proxy rotation.
Everything supported by our airnewzealand.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 orchestration and deduplication. Playwright handles full JavaScript rendering, cookie sessions, and the complex booking flow interactions.
We maintain pools of residential ISP proxies across NZ, AU, and US regions to capture geo-specific fares and bypass IP-based rate limiting.
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 airnewzealand.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing is generally permissible. DataFlirt targets only public, non-authenticated route and fare data. We do not extract personal data or circumvent authentication walls for PNRs. Clients should review Air New Zealand's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic TLS fingerprints, and request timing modelled on human behaviour to bypass enterprise-grade bot mitigation like Akamai or Datadome.
Yes. We route requests through specific country nodes using our residential proxy network to capture accurate point-of-sale pricing, ensuring you see the fares exactly as local customers do.
Pipeline frequency is configurable. We can run daily sweeps across large route networks or execute high-frequency hourly checks on specific high-value routes to monitor dynamic yield changes.
Yes. We extract pricing for all available tiers on a given flight, including Seat, Seat+Bag, The Works, Works Deluxe, Premium Economy, and Business Premier.
We capture time-series data from the day your pipeline starts. Every run produces timestamped snapshots, allowing you to build historical models of fare bucket depletion.
Engagements typically start with a defined list of origin/destination pairs and specific forward-looking date ranges. Contact us with your route volume and frequency requirements for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily sweep of trans-Tasman routes or high-frequency fare monitoring across the Pacific network, we scope, build, and operate the pipeline. Tell us what you need.
