We extract flight schedules, dynamic pricing, ancillary fees, and route networks from Suncountry. 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 suncountry.com. All fields typed and schema-versioned.
"flight_number": "SY 241", "origin_iata": "MSP", "destination_iata": "MCO", "departure_time_local": "2026-11-04T07:15:00", "arrival_time_local": "2026-11-04T11:30:00", "duration_minutes": 195, "aircraft_type": "Boeing 737-800", "stops": 0, "is_red_eye": false
| # | flight_number | origin_iata | destination_iata | departure_time_local | arrival_time_local | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from suncountry.com. All fields typed and schema-versioned.
"flight_number": "SY 241", "departure_date": "2026-11-04", "base_fare_usd": 89.0, "taxes_fees_usd": 21.4, "total_fare_usd": 110.4, "currency": "USD", "fare_class": "Economy", "seats_remaining": 4, "price_timestamp": "2026-10-01T14:22:10Z"
| # | flight_number | departure_date | base_fare_usd | taxes_fees_usd | total_fare_usd | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Ancillary Fees objects from suncountry.com. All fields typed and schema-versioned.
"flight_number": "SY 241", "departure_date": "2026-11-04", "carry_on_bag_fee": 35.0, "checked_bag_1_fee": 30.0, "standard_seat_fee_min": 12.0, "standard_seat_fee_max": 25.0, "priority_boarding_fee": 15.0, "bundle_good_price": 45.0
| # | flight_number | departure_date | carry_on_bag_fee | checked_bag_1_fee | checked_bag_2_fee | standard_seat_fee_min |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from suncountry.com. All fields typed and schema-versioned.
"origin_iata": "MSP", "destination_iata": "CUN", "is_seasonal": true, "season_start_date": "2026-11-15", "season_end_date": "2027-04-30", "frequency_per_week": 5, "distance_miles": 1680, "route_status": "Active"
| # | origin_iata | destination_iata | is_seasonal | season_start_date | season_end_date | frequency_per_week |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Flight Status objects from suncountry.com. All fields typed and schema-versioned.
"flight_number": "SY 241", "flight_date": "2026-10-01", "status": "Delayed", "scheduled_departure": "2026-10-01T07:15:00", "actual_departure": "2026-10-01T08:05:00", "delay_minutes": 50, "departure_gate": "H10", "arrival_gate": "72"
| # | flight_number | flight_date | tail_number | status | scheduled_departure | actual_departure |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Suncountry scraper handles the entire booking flow: flight schedules, dynamic fare matrices, ancillary fee extraction, and route availability. Built with JavaScript rendering and anti-bot circumvention.
Flight numbers, departure times, arrival times, aircraft types, and connection details across the entire network.
Capture base fares, taxes, and total costs across multiple dates and fare classes simultaneously.
Extract carry-on costs, checked baggage tiers, seat selection minimums, and bundle pricing specific to each route.
Monitor new route announcements, seasonal start dates, and frequency adjustments across the Suncountry network.
Track points pricing and member-exclusive discounts alongside standard cash fares.
Monitor seats remaining thresholds and sold-out statuses to model flight load factors.
Maintain time-series data of fare fluctuations from schedule release to departure date.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences.
Only receive records when fares or schedules change, reducing compute and storage bloat.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, or full network requirements. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for suncountry.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines invest heavily in scraping detection to protect pricing data. Here is how we stay resilient.
Airline booking engines use strict rate limiting and TLS fingerprinting. Our crawlers use US residential ISP proxies with realistic browser fingerprints and full cookie session management to blend in.
Suncountry relies on single-page application frameworks to load fare matrices. We run full Playwright browser sessions to execute JavaScript and hydrate dynamic price widgets.
Extracting ancillary fees requires advancing through the booking funnel. Our system maintains session state, cookies, and tokens to reach the seat selection and baggage fee pages.
Airlines update their UI frequently. Our selector strategy uses multiple fallback chains per field, including JSON payload interception where possible, ensuring layout changes do not break the pipeline.
For large route networks, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, providing a clean changelog rather than full re-dumps.
Rival airlines and OTAs monitor Suncountry base fares and ancillary fees to optimise their own pricing strategies.
Aviation analysts track seasonal route deployment and flight frequencies to identify market gaps and capacity trends.
Revenue management teams correlate price changes with departure proximity to reverse-engineer ULCC pricing curves.
Metasearch engines integrate direct schedule and pricing data to supplement GDS feeds.
Consultancies track baggage and seat selection fee adjustments to benchmark ULCC revenue models.
Financial analysts track sold-out indicators and frequency changes to forecast quarterly passenger volumes.
"Suncountry pricing data is highly dynamic, driven by ancillary fees and seasonal routes. Capturing the true cost requires traversing the full booking flow."
Most teams underestimate the investment required to scrape airline pricing. Reliable extraction requires residential proxies, full JavaScript rendering for fare matrices, and session management. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our suncountry.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, cookie sessions, and interaction flows required for booking engines.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to maintain state through the booking funnel.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About suncountry.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 data. We do not extract personal data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for 503 rate spikes in real time.
Yes. Our crawlers navigate the booking flow past the initial search results to capture route-specific baggage fees, seat selection minimums, and bundle pricing.
Pipelines can be configured to run daily, hourly, or at custom intervals based on your requirements. Real-time streaming is available for specific target routes.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per route and departure date from the date your pipeline starts.
Our smallest packages start at a defined route list with daily delivery. For full network coverage or custom schema requirements, we price based on volume and delivery frequency.
Yes. We build bespoke pipelines for major carriers, ULCCs, and OTAs globally.
Absolutely. We provide a sample run of specific 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 daily route snapshot or continuous price monitoring across 5,000 flight paths, we build and operate the pipeline. Tell us what you need.
