We extract flight schedules, dynamic fares, Extra Comfort availability, and HawaiianMiles redemption rates. 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 hawaiianairlines.com. All fields typed and schema-versioned.
"flight_number": "HA17", "origin_iata": "LAS", "destination_iata": "HNL", "departure_time_local": "2026-08-14T08:00:00", "arrival_time_local": "2026-08-14T11:15:00", "duration_minutes": 375, "aircraft_type": "Airbus A330-200"
| # | flight_number | origin_iata | destination_iata | departure_time_local | arrival_time_local | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Fares objects from hawaiianairlines.com. All fields typed and schema-versioned.
"flight_number": "HA17", "departure_date": "2026-08-14", "main_cabin_price": 249.0, "extra_comfort_price": 349.0, "first_class_price": 899.0, "currency": "USD", "hawaiianmiles_price": 20000, "scrape_timestamp": "2026-05-12T10:15:00Z"
| # | flight_number | departure_date | main_cabin_price | extra_comfort_price | first_class_price | hawaiianmiles_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Seat Availability objects from hawaiianairlines.com. All fields typed and schema-versioned.
"flight_number": "HA17", "departure_date": "2026-08-14", "total_seats": 278, "available_seats": 42, "extra_comfort_available": 12, "first_class_available": 2, "occupied_seats": 230
| # | flight_number | departure_date | total_seats | available_seats | extra_comfort_available | first_class_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Route Network objects from hawaiianairlines.com. All fields typed and schema-versioned.
"origin_code": "HNL", "origin_name": "Honolulu", "destination_code": "OGG", "destination_name": "Kahului", "distance_miles": 100, "direct_flight": true, "inter_island": true
| # | origin_code | origin_name | destination_code | destination_name | distance_miles | frequency_per_week |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Flight Status objects from hawaiianairlines.com. All fields typed and schema-versioned.
"flight_number": "HA17", "date": "2026-08-14", "status": "On Time", "scheduled_departure": "08:00 AM", "estimated_departure": "08:00 AM", "gate_departure": "D14", "baggage_claim": "Carousel 4"
| # | flight_number | date | scheduled_departure | estimated_departure | scheduled_arrival | estimated_arrival |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our pipeline handles the complexities of airline booking engines: dynamic pricing grids, session-based seatmaps, and multi-currency fare classes.
Extract schedules and fares across all Hawaiian Airlines routes, from short inter-island hops to long-haul international flights.
Capture Main Cabin, Extra Comfort, and First Class pricing grids. Track price fluctuations over time for yield management analysis.
Extract point requirements for award flights, identifying low-level and high-level redemption availability.
Monitor seat inventory by cabin class. Detect blocked seats, occupied seats, and remaining Extra Comfort upgrades.
Track baggage fees, priority boarding costs, and other ancillary revenue drivers attached to specific fare classes.
Monitor delays, gate changes, and estimated arrival times across the entire Hawaiian Airlines fleet.
Identify flights operated by partner airlines (e.g., Japan Airlines, JetBlue) sold through the Hawaiian Airlines portal.
Scrape 330-day forward-looking schedule and pricing matrices for capacity planning.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences.
Brief in. Clean data out.
Provide origin-destination pairs, travel dates, or flight numbers. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for hawaiianairlines.com.
Schema validation, null-rate checks, price-outlier detection, and sample seatmaps before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airline booking engines use complex session states and aggressive rate limiting. Here is how we stay resilient.
Airline sites block datacentre IPs aggressively. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Booking flows are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution to navigate the fare selection calendar.
Extracting seatmaps requires maintaining session state from the initial search through the fare selection step. We manage these multi-step flows reliably.
For large route networks, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and storage bloat.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops.
Competing airlines monitor Hawaiian Airlines fare changes on overlapping routes to optimise their own revenue management systems.
OTAs and metasearch engines ingest direct pricing and availability data to supplement GDS feeds.
Aviation analysts track frequency changes and seat capacity on inter-island and transpacific routes.
Points optimisers track HawaiianMiles redemption rates to identify sweet spots and devaluation trends.
Researchers analyse the pricing delta between Main Cabin and Extra Comfort as departure dates approach.
Insurance and logistics firms monitor flight status data to trigger automated delay compensation or reroute cargo.
"Hawaiian Airlines controls the inter-island and mainland-to-Hawaii market, making their pricing and availability data critical for Pacific travel intelligence."
Most teams underestimate the investment required: reliable airline scraping requires residential proxies, full JavaScript rendering for booking flows, session maintenance, daily selector updates, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our hawaiianairlines.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. Rotation happens per-request with sticky sessions where required for booking flows. 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 hawaiianairlines.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and schedule information is generally permissible. DataFlirt targets only public, non-authenticated flight data. We do not extract personal data or circumvent authentication walls. Clients should 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. We monitor for rate spikes in real time and trigger pool rotation automatically.
Yes. We capture pricing across all available fare classes, including Main Cabin, Extra Comfort, and First Class, as well as HawaiianMiles redemption rates.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined route set. Full network refreshes at daily cadence complete within a 4-8 hour window.
Yes. We maintain a time-series record per flight for seat availability, fare changes, and cabin class inventory from the date your pipeline starts.
Our smallest packages start at a defined route list (typically 50-200 O&D pairs) with daily delivery. For larger networks, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off route network dump or a continuous price-monitoring feed across 1,000 flights, we scope, build, and operate the pipeline. Tell us what you need.
