We extract flight schedules, dynamic pricing, fare families, and route availability from s7.ru. 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 s7.ru. All fields typed and schema-versioned.
"flight_number": "S7 2505", "origin_iata": "DME", "destination_iata": "OVB", "departure_time_local": "2026-08-14T23:55:00", "arrival_time_local": "2026-08-15T07:55:00", "duration_minutes": 240, "aircraft_type": "Airbus A320neo", "operating_carrier": "S7 Airlines"
| # | flight_number | origin_iata | destination_iata | departure_time_local | arrival_time_local | duration_minutes |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Fares objects from s7.ru. All fields typed and schema-versioned.
"flight_number": "S7 2505", "departure_date": "2026-08-14", "fare_family": "Economy Standard", "price_amount": 14500.0, "currency": "RUB", "miles_accrual": 850, "tickets_remaining": 4, "refundable": false
| # | flight_number | departure_date | fare_family | booking_class | price_amount | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Route Availability objects from s7.ru. All fields typed and schema-versioned.
"origin": "LED", "destination": "AER", "direct_flights_count": 2, "connecting_flights_count": 5, "connection_airports": "['DME', 'TOL']", "min_connection_time_mins": 95, "active_season": "Summer 2026"
| # | origin | destination | direct_flights_count | connecting_flights_count | connection_airports | min_connection_time_mins |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Fleet & Aircraft objects from s7.ru. All fields typed and schema-versioned.
"aircraft_code": "32N", "model_name": "A320neo", "manufacturer": "Airbus", "passenger_capacity": 164, "business_class_seats": 8, "economy_class_seats": 156, "wifi_available": false
| # | aircraft_code | model_name | manufacturer | passenger_capacity | layout_type | business_class_seats |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Baggage & Ancillaries objects from s7.ru. All fields typed and schema-versioned.
"fare_class": "Economy Basic", "cabin_bag_kg": 10, "checked_bag_kg": 0, "extra_bag_price": 2500.0, "seat_selection_min_price": 400.0, "meal_included": false, "currency": "RUB"
| # | fare_class | cabin_bag_kg | cabin_bag_dimensions | checked_bag_kg | extra_bag_price | seat_selection_min_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our S7.ru scraper bypasses airline WAFs to extract schedules, pricing matrices, and ancillary rules across the entire network.
Extract origin-destination pairs, flight numbers, aircraft types, and operating carriers across the entire S7 network.
Capture real-time pricing across Basic, Standard, and Plus fare families with currency normalisation.
Track tickets remaining warnings and class availability for yield management analysis.
Extract costs for extra baggage, seat selection, and sports equipment across different routes.
Map miles accrual rates and redemption costs for specific flights and fare classes.
Identify flights operated by partner airlines versus S7 metal.
Calculate layover durations, terminal changes, and minimum connection times for multi-leg journeys.
Extract fares in RUB, EUR, USD, and other supported currencies directly from the booking engine.
Run daily schedule syncs or high-frequency price checks on competitive routes.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and fare types. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for s7.ru.
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.
Airline pricing engines use aggressive caching and bot protection. Here is how we ensure data accuracy.
Airline sites use strict WAFs. We route requests through residential proxies in target regions to avoid IP bans and CAPTCHA walls.
S7's booking flow requires maintaining search session tokens. Our crawlers manage these stateful interactions to reach deep pricing data.
Pricing grids load asynchronously. We execute JavaScript to hydrate the fare matrices before extraction.
Airlines cache popular routes. We use specific search patterns to force live pricing engine lookups rather than stale cache responses.
Booking engine DOMs change during promotions. We use multi-layer fallback selectors to maintain extraction integrity.
OTAs and competitor airlines monitor S7 pricing to adjust their own yield management algorithms.
Aviation analysts track schedule changes, frequency adjustments, and new route launches.
Revenue teams analyse the price gaps between Basic, Standard, and Plus fares across different markets.
Metasearch engines integrate direct schedule and pricing data to supplement GDS feeds.
Track how S7 prices baggage and seat selection based on route distance and demand.
Monitor S7 Priority miles requirements for reward flights to model loyalty programme liability.
"Airline pricing is the ultimate dynamic dataset. Without a managed pipeline, you are making yield management decisions on stale cached data."
Extracting flight data requires navigating complex booking funnels, maintaining session state, and bypassing aggressive WAFs. DataFlirt absorbs that complexity so your revenue and analytics teams can focus on pricing strategy, not proxy rotation.
Everything supported by our s7.ru scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Handles asynchronous booking flows and DOM hydration.
Bypasses airline WAFs using regional residential IPs.
Lambda and ECS handle burst scaling during peak schedule syncs.
Data delivered to where your team already works — no new tooling required.
Scraping public flight data is generally permissible. We target unauthenticated schedules and prices.
We use residential proxies and realistic browser fingerprints to bypass bot detection.
Yes, we capture Basic, Standard, and Plus tiers simultaneously for every route.
Real-time pipelines can check specific origin-destination pairs on demand.
Yes, we extract full itineraries including layover times and terminal changes.
We start at defined route lists with daily delivery. Custom frequencies are available.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily schedule dump or continuous price monitoring across 500 routes, we build and operate the pipeline.
