We extract flight schedules, tiered pricing, seat availability, and route networks from Pegasus Airlines. 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 pegasusairlines.com. All fields typed and schema-versioned.
"flight_number": "PC1134", "origin_code": "SAW", "destination_code": "CDG", "departure_time_local": "2026-05-12T10:15:00", "arrival_time_local": "2026-05-12T13:00:00", "duration_minutes": 225, "aircraft_type": "Airbus A320neo", "stops": 0
| # | flight_number | origin_code | destination_code | departure_time_local | arrival_time_local | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Ticket Pricing objects from pegasusairlines.com. All fields typed and schema-versioned.
"flight_number": "PC1134", "departure_date": "2026-05-12", "tier_name": "Essentials", "price_total": 145.5, "currency": "EUR", "seats_remaining": 4, "scrape_timestamp": "2026-04-01T08:12:44Z"
| # | flight_number | departure_date | tier_name | price_total | currency | base_fare |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Route Network objects from pegasusairlines.com. All fields typed and schema-versioned.
"origin_code": "SAW", "origin_city": "Istanbul", "destination_code": "LHR", "destination_city": "London", "direct_flight": true, "frequency_weekly": 14, "active_status": true
| # | origin_code | origin_city | destination_code | destination_city | distance_km | frequency_weekly |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Flight Status objects from pegasusairlines.com. All fields typed and schema-versioned.
"flight_number": "PC1134", "flight_date": "2026-05-12", "scheduled_departure": "10:15:00", "estimated_departure": "10:45:00", "status": "Delayed", "terminal": "1", "delay_minutes": 30
| # | flight_number | flight_date | scheduled_departure | estimated_departure | actual_departure | status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Ancillary Fees objects from pegasusairlines.com. All fields typed and schema-versioned.
"route_id": "SAW-CDG", "baggage_20kg_price": 25.0, "seat_selection_standard": 8.0, "seat_selection_extra_legroom": 18.0, "meal_standard_price": 12.5, "currency": "EUR", "priority_boarding_price": 15.0
| # | route_id | baggage_20kg_price | seat_selection_standard | seat_selection_extra_legroom | meal_standard_price | priority_boarding_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Pegasus scraper handles multi-step search forms, dynamic pricing tiers, and aggressive bot protection to deliver normalised flight data.
Capture departure times, arrival times, aircraft types, and flight durations across the entire Pegasus network.
Extract prices for Light, Essentials, Advantage, and Comfort Flex tiers simultaneously for accurate competitive analysis.
Monitor 'seats remaining' indicators to gauge flight capacity and demand velocity over time.
Route requests through regional proxy pools to capture point-of-sale pricing differences across European and Asian markets.
Extract costs for extra baggage, seat selection, and in-flight meals tied to specific routes and fare classes.
Parse connecting flights through SAW (Sabiha Gokcen) including layover durations and combined pricing.
Track delays, gate changes, and actual departure times for operational intelligence.
Extract base fares and taxes in TRY, EUR, GBP, or USD based on specified point-of-sale parameters.
Navigate airline bot protection using TLS fingerprinting and residential proxy rotation to ensure zero blocked requests.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and currency requirements. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session state management for pegasusairlines.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines deploy complex session states and bot protection. Here is how we maintain reliable data extraction.
Pegasus uses enterprise bot protection. Our infrastructure rotates TLS fingerprints, manages HTTP/2 pseudo-headers, and utilises residential IPs to appear as legitimate traveler traffic.
Flight searches require maintaining cookie state across multiple POST requests. We use Playwright to execute the exact sequence of a human user, capturing the final pricing payload.
The booking engine layout changes based on flight availability and route type. Our selectors use fallback chains to ensure data is captured regardless of promotional popups or layout shifts.
Airlines alter prices based on the user IP. We route requests through specific country proxies to capture the exact price a user in London or Istanbul would see.
To avoid triggering rate limits on specific origin-destination pairs, our scheduler distributes requests across a wide IP pool with randomised delays.
Online Travel Agencies aggregate Pegasus fares to display complete low-cost carrier options to their users.
Competing airlines track Pegasus pricing on overlapping routes to optimise their own dynamic pricing algorithms.
Tourism boards and analysts monitor flight frequencies and capacity adjustments to forecast regional travel demand.
Revenue management teams analyse how Pegasus adjusts fare tiers closer to departure dates.
Aviation consultants track ancillary fee structures and base fares to estimate route-level margins.
Corporate travel managers track live flight status and delay patterns across the SAW hub.
"Pegasus Airlines operates a highly dynamic pricing model across its network. Extracting accurate fare tiers requires continuous stateful session management."
Airlines deploy aggressive anti-bot measures to protect their pricing data. Scraping Pegasus requires handling Akamai bot protection, geographic pricing variances, and complex multi-step search forms. DataFlirt manages this infrastructure so your data engineering team receives normalised pricing data without dealing with session timeouts or IP bans.
Everything supported by our pegasusairlines.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 the stateful multi-step search forms required by airline booking engines.
We maintain pools of residential ISP proxies across target regions to capture accurate point-of-sale pricing without triggering rate limits.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling for high-frequency price monitoring. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About pegasusairlines.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 search results. We do not extract personal data, access BolBol accounts, or interact with existing PNRs. Clients should review airline terms of service and consult legal counsel for specific use cases.
We use residential ISP proxies, TLS fingerprint spoofing, and full Playwright browser sessions to emulate legitimate user traffic. This prevents Akamai and Cloudflare from blocking our extraction nodes.
Yes. Our pipeline extracts pricing for Light, Essentials, Advantage, and Comfort Flex tiers simultaneously for every searched route.
We can configure pipelines to run at hourly, daily, or weekly cadences depending on your requirements. High-frequency runs are distributed across proxy pools to ensure stability.
Yes. We can route requests through specific geographic proxies (e.g., UK, Turkey, Germany) to capture the exact price displayed to users in those regions.
Our smallest packages start at a defined route list (typically 100-500 origin-destination pairs) monitored daily. We price based on request volume and delivery frequency.
Yes. We provide a sample run of up to 20 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 daily route network updates or hourly price monitoring across key corridors, we scope, build, and operate the pipeline. Tell us what you need.
