We extract flight schedules, dynamic fare pricing, Club Jetstar rates, seat availability, and ancillary fees from Jetstar. 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 jetstar.com. All fields typed and schema-versioned.
"flight_number": "JQ501", "origin": "SYD", "destination": "MEL", "departure_time": "2024-11-12T06:00:00Z", "arrival_time": "2024-11-12T07:35:00Z", "duration_minutes": 95, "aircraft_type": "A320", "stops": 0
| # | flight_number | origin | destination | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fare Pricing objects from jetstar.com. All fields typed and schema-versioned.
"flight_number": "JQ501", "currency": "AUD", "starter_fare": 89.0, "plus_bundle": 139.0, "max_bundle": 219.0, "club_jetstar_fare": 75.0, "taxes_included": true, "price_timestamp": "2024-10-01T14:22:11Z"
| # | flight_number | departure_date | currency | starter_fare | plus_bundle | max_bundle |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ancillary Fees objects from jetstar.com. All fields typed and schema-versioned.
"route": "SYD-MEL", "carry_on_allowance": "7kg", "checked_bag_15kg_fee": 25.0, "checked_bag_20kg_fee": 29.0, "standard_seat_fee": 7.0, "extra_legroom_fee": 25.0, "meal_preorder_fee": 15.0
| # | flight_number | route | carry_on_allowance | checked_bag_15kg_fee | checked_bag_20kg_fee | checked_bag_30kg_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from jetstar.com. All fields typed and schema-versioned.
"origin_code": "MEL", "destination_code": "DPS", "region": "Asia Pacific", "direct_flights_weekly": 14, "seasonal_route": false, "distance_km": 4380, "start_date": "2024-01-01"
| # | origin_airport | origin_code | destination_airport | destination_code | region | direct_flights_weekly |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Flight Status objects from jetstar.com. All fields typed and schema-versioned.
"flight_number": "JQ43", "origin": "MEL", "destination": "DPS", "scheduled_departure": "2024-10-01T09:15:00Z", "status": "Delayed", "estimated_departure": "2024-10-01T10:30:00Z", "gate": "15"
| # | date | flight_number | origin | destination | scheduled_departure | estimated_departure |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Jetstar scraper handles every layer of the booking engine: flight schedules, dynamic pricing matrices, Club Jetstar rates, and ancillary fees, with full JavaScript execution and WAF circumvention built in.
Extract flight numbers, origin-destination pairs, departure and arrival timings, duration, and aircraft types across the entire network.
Capture base Starter fares alongside Plus, Max, and Flex bundle pricing for deep competitor analysis.
Extract member-only discounted rates to understand the delta between public fares and loyalty pricing.
Track dynamic pricing for 15kg, 20kg, and 30kg checked baggage, plus standard and extra-legroom seat selection fees.
Capture fares in AUD, NZD, SGD, JPY, and other regional currencies using geo-targeted residential IP proxies.
Identify flights operated by Jetstar Airways, Jetstar Asia, Jetstar Japan, or Qantas code-share partners.
Track real-time flight status, estimated departure changes, and gate information for operational analysis.
Extract entire months of low-fare finder data in a single pass to build comprehensive price prediction models.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and required currencies. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and anti-bot handling for jetstar.com.
Schema validation, null-rate checks, price-outlier detection, and sample data review 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 their pricing data. Here is how we stay resilient.
Airlines use aggressive Web Application Firewalls like Akamai and Cloudflare. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full TLS spoofing to bypass these checks.
Jetstar's booking engine is a complex single-page application. We run full Playwright browser sessions with JavaScript execution and XHR interception to extract data from the underlying API responses.
Pricing changes based on the user's geographic location and selected currency. We maintain sticky sessions with specific residential IPs to ensure consistent and accurate multi-currency extraction.
For large route networks, we maintain a hash index of last-seen values per flight. Subsequent runs only push price diffs, reducing compute cost and downstream processing load.
To avoid triggering rate limits on specific origin-destination searches, we distribute the workload across thousands of distinct IPs, ensuring high throughput without blocking.
Online Travel Agencies monitor direct airline rates against their own inventory to ensure price competitiveness.
Competing airlines track Jetstar's low-cost carrier pricing strategies across shared routes to adjust their own yield management.
Travel agents extract live flight pricing to build custom holiday bundles combining flights, hotels, and transfers.
Aviation analysts track flight frequency, aircraft deployment, and pricing trends to model route profitability.
Industry researchers track dynamic baggage and seat fee pricing to benchmark ancillary revenue strategies.
Machine learning teams use historical flight status and delay data to train predictive models for travel insurance products.
"Jetstar's dynamic pricing and ancillary fee structures hold critical market intelligence for the APAC aviation sector — but extracting it requires defeating enterprise anti-bot systems."
Aviation data extraction is notoriously hostile. Airlines deploy aggressive rate limiting, session-based pricing, and complex single-page applications to prevent automated collection. DataFlirt manages the residential proxy rotation, browser fingerprinting, and JavaScript execution required to extract clean fare data at scale.
Everything supported by our jetstar.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 and retry logic. Playwright handles JavaScript rendering, XHR interception, and interaction flows required by airline booking engines.
We maintain pools of residential ISP proxies across APAC regions. Rotation happens per-request with sticky sessions where currency and geo-targeting are required.
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 jetstar.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing data is generally permissible. DataFlirt targets only public, non-authenticated booking engine data. We do not extract personal passenger data or circumvent authentication walls. Clients should review airline ToS and 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 to bypass WAF protections like Akamai and Cloudflare.
We support data extraction across the entire network, including Jetstar Airways (Australia/NZ), Jetstar Asia (Singapore), and Jetstar Japan.
Real-time streaming pipelines achieve sub-30-minute latency for price updates on a defined origin-destination set. Full network refreshes complete within a 4-8 hour window depending on scale.
Yes. We configure our crawler sessions to request specific currencies (AUD, SGD, JPY, etc.) using geo-targeted proxies to ensure accurate local pricing.
Our smallest packages start at a defined list of 500 origin-destination pairs monitored daily. For larger networks or high-frequency intra-day tracking, we price based on volume.
Absolutely. We provide a sample run of up to 50 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 route schedule dump or a continuous fare-monitoring feed across 5,000 OD pairs — we scope, build, and operate the pipeline. Tell us what you need.
