We extract flight schedules, dynamic fare pricing, seat availability, aircraft metadata, and route networks from Aeroflot. 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 aeroflot.ru. All fields typed and schema-versioned.
"flight_number": "SU1492", "origin_airport": "SVO", "destination_airport": "AER", "departure_time": "2026-10-12T14:30:00Z", "arrival_time": "2026-10-12T18:15:00Z", "duration_minutes": 225, "aircraft_type": "Sukhoi Superjet 100"
| # | flight_number | origin_airport | destination_airport | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from aeroflot.ru. All fields typed and schema-versioned.
"flight_number": "SU1492", "departure_date": "2026-10-12", "fare_family": "Economy Promo", "price_total": 14500.0, "currency": "RUB", "tax_amount": 1200.0, "base_fare": 13300.0, "seats_remaining": 4
| # | flight_number | departure_date | fare_family | fare_basis | price_total | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from aeroflot.ru. All fields typed and schema-versioned.
"origin_iata": "SVO", "destination_iata": "AER", "distance_km": 1370, "direct_flight": true, "frequency_per_week": 21, "seasonal_route": false, "codeshare_partners": "['Rossiya Airlines']"
| # | origin_iata | destination_iata | distance_km | direct_flight | frequency_per_week | seasonal_route |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Flight Status objects from aeroflot.ru. All fields typed and schema-versioned.
"flight_number": "SU1492", "flight_date": "2026-10-12", "scheduled_departure": "14:30", "estimated_departure": "14:45", "current_status": "Delayed", "terminal": "B", "gate": "114", "baggage_carousel": "4"
| # | flight_number | flight_date | scheduled_departure | estimated_departure | actual_departure | current_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Aircraft Metadata objects from aeroflot.ru. All fields typed and schema-versioned.
"tail_number": "RA-89098", "aircraft_model": "Sukhoi Superjet 100", "manufacturer": "Sukhoi", "cabin_configuration": "2-class", "economy_seats": 75, "business_seats": 12, "comfort_seats": 0, "wifi_available": false
| # | tail_number | aircraft_model | manufacturer | cabin_configuration | economy_seats | business_seats |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Aeroflot scraper navigates complex booking flows, bypasses airline anti-bot systems, and extracts accurate pricing across all fare families and cabin classes.
Extract timetables, direct routes, and connecting itineraries across the entire Aeroflot and Rossiya Airlines network.
Capture total price, base fare, and taxes across all fare families from Economy Promo to Business Maximum.
Monitor remaining seats per fare bucket to estimate load factors and track inventory depletion.
Track scheduled, estimated, and actual departure times, alongside terminal and gate assignments.
Extract equipment types, cabin configurations, and operating carrier details for every scheduled flight.
Capture point-of-sale specific pricing by routing requests through region-specific proxy infrastructure.
Extract included baggage allowances, seat selection fees, and refund policies tied to specific fare rules.
Identify flights operated by Aurora, Rossiya Airlines, and other active codeshare partners.
Run daily network sweeps or configure hourly price checks on highly competitive trunk routes.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and required fare classes. We design the extraction schema together.
We configure Playwright crawlers, regional proxy rotation, session management, and CAPTCHA handling for aeroflot.ru.
Schema validation, null-rate checks, price anomaly detection, and currency normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airline pricing engines are notoriously difficult to scrape. Here is how we maintain stability against complex booking flows and bot mitigation.
Airlines frequently alter pricing based on the user's IP location. We utilise residential proxy pools to simulate searches from specific regions, ensuring you capture accurate point-of-sale fares and bypass aggressive geo-blocking.
Extracting final tax-inclusive prices requires navigating multi-step booking funnels. Our infrastructure maintains cookie state and session tokens across sequential requests to reach the final fare breakdown.
Modern airline frontends rely heavily on asynchronous API calls to load pricing. We run full Playwright browser sessions to execute JavaScript, wait for network idle states, and capture the final rendered DOM.
Aviation booking engines impose strict rate limits to protect inventory systems. We distribute requests across thousands of IPs with randomised timing intervals to avoid triggering velocity blocks.
Airline APIs occasionally return empty responses or zero-value prices during maintenance windows. Our validation layer catches these anomalies and automatically queues retries before delivering data to your warehouse.
Online travel agencies monitor direct-channel pricing on aeroflot.ru to ensure competitive positioning and parity compliance.
Rival carriers track Aeroflot's fare adjustments, route frequencies, and capacity changes to optimise their own network strategies.
Metasearch engines ingest schedule and pricing data to populate flight comparison matrices for end consumers.
Aviation analysts track seat availability depletion rates to estimate load factors and route profitability.
Travel management companies audit booked corporate fares against public availability to ensure optimal procurement.
Logistics and travel insurance providers track real-time delay and cancellation data to trigger automated customer communications.
"Airline pricing is inherently volatile. Aeroflot's fare families require deep session execution to extract accurately at scale."
Extracting aviation data requires navigating complex multi-step booking flows, managing strict rate limits, and handling point-of-sale geo-restrictions. DataFlirt manages the proxy rotation, session state, and schema normalisation so your data engineering team receives clean, queryable route and pricing data without maintaining fragile web scrapers.
Everything supported by our aeroflot.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.
Scrapy handles route orchestration and deduplication. Playwright manages JavaScript execution, cookie state, and complex booking funnel interactions.
We route requests through ISP-grade residential IPs to simulate regional searches, bypass geo-blocks, and capture accurate point-of-sale pricing.
Pipelines run on AWS Lambda and ECS. Airflow manages scheduling and dependencies, ensuring scheduled sweeps and hourly price checks execute flawlessly.
Data delivered to where your team already works — no new tooling required.
About aeroflot.ru scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing from aeroflot.ru is generally permissible under standard web scraping legal precedents. DataFlirt targets only public, non-authenticated timetable and fare data. We do not extract PNRs, bypass authentication for Aeroflot Bonus accounts, or extract PII. Clients should review their own compliance requirements.
We maintain diverse proxy pools, including residential IPs within specific target regions, to bypass geo-restrictions and ensure continuous access to aeroflot.ru regardless of external network blocking.
Yes. Our pipeline extracts the full fare matrix presented during the booking flow, capturing the base price, taxes, and specific rules for every available fare family on a given flight.
For critical routes, we configure hourly polling to capture dynamic price adjustments. Full network sweeps typically run on a daily cadence. Delivery latency is minimal once the extraction completes.
We extract the remaining seat count indicator displayed for specific fare buckets (e.g. '3 seats left at this price'). We cannot extract total unbooked aircraft capacity, as this is internal inventory data.
Engagements typically start with a defined list of origin-destination pairs and a set delivery frequency. We scope the pipeline based on the required request volume and update cadence.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily schedule dump or continuous price monitoring across key routes, we scope, build, and operate the pipeline. Tell us what you need.
