We extract flight schedules, dynamic pricing, fare classes, and route availability from RwandAir. 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 rwandair.com. All fields typed and schema-versioned.
"flight_number": "WB700", "origin_code": "KGL", "destination_code": "LHR", "departure_time": "2026-08-14T23:30:00Z", "arrival_time": "2026-08-15T06:30:00Z", "duration_minutes": 540, "aircraft_type": "A330-300", "stops": 0
| # | flight_number | origin_code | destination_code | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from rwandair.com. All fields typed and schema-versioned.
"flight_number": "WB700", "cabin_class": "Economy", "fare_basis_code": "YLE1RWA", "total_price": 485.5, "base_fare": 320.0, "tax_amount": 165.5, "currency": "USD", "refundable": false
| # | flight_number | search_date | cabin_class | fare_basis_code | total_price | base_fare |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from rwandair.com. All fields typed and schema-versioned.
"origin_code": "KGL", "destination_code": "CDG", "origin_city": "Kigali", "destination_city": "Paris", "direct_flight": true, "frequency_per_week": 3, "season_start": "2026-03-29", "season_end": "2026-10-24"
| # | origin_code | destination_code | origin_city | destination_city | distance_km | direct_flight |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Availability objects from rwandair.com. All fields typed and schema-versioned.
"flight_number": "WB302", "departure_date": "2026-08-14", "economy_seats_available": 4, "business_seats_available": 9, "premium_seats_available": 0, "waitlist_open": false, "flight_status": "SCHEDULED", "equipment_change": false
| # | flight_number | departure_date | economy_seats_available | business_seats_available | premium_seats_available | waitlist_open |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ancillaries & Baggage objects from rwandair.com. All fields typed and schema-versioned.
"flight_number": "WB700", "cabin_class": "Business", "checked_bags_allowed": 2, "checked_bag_weight_kg": 32, "cabin_bags_allowed": 2, "meal_included": true, "lounge_access_included": true, "wifi_available": true
| # | flight_number | cabin_class | checked_bags_allowed | checked_bag_weight_kg | cabin_bags_allowed | excess_baggage_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our pipeline handles the complexities of airline booking engines: session token management, multi-step search flows, dynamic pricing grids, and currency normalisation.
Extract flight numbers, departure/arrival times, aircraft types, and stopover information for the entire RwandAir network.
Capture pricing across Economy, Premium Economy, and Business classes. Track price fluctuations across multiple departure dates.
Extract native pricing in RWF, USD, GBP, EUR, and other local currencies, normalising to your preferred base currency.
Map complex itineraries through the Kigali hub, calculating total layover times and multi-leg pricing.
Extract cancellation policies, change fees, and refundability rules associated with specific fare basis codes.
Monitor remaining seat counts per cabin class to estimate flight load factors and booking velocity.
Extract frequent flyer redemption requirements, tracking the miles needed for specific routes and dates.
Capture data on checked baggage allowances, excess weight fees, and seat selection costs.
Run one-off network exports or configure continuous pipelines at hourly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide Origin-Destination pairs, date ranges, and cabin classes. We design the extraction schema together.
We configure Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for rwandair.com.
Schema validation, null-rate checks, price-outlier detection, and timezone normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airline booking engines are designed to block automated queries. Here is how we maintain reliable extraction.
Airlines use strict WAFs. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management, trained on real user booking behaviour.
Flight searches require complex cookie chains and token exchanges. We maintain session state across the entire search and pricing flow to access final fare grids.
RwandAir's booking engine uses heavy JavaScript for pricing grids. We run full browser sessions to trigger lazy-loading and hydrate price widgets.
Airlines price in local currencies based on origin. We extract the native currency and apply consistent normalisation mapping for downstream analysis.
For large route networks, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Feed accurate RwandAir pricing and availability data into flight aggregators and online travel agencies.
Other African and international carriers monitor Kigali routes to adjust their own pricing and capacity.
Aviation analysts track flight frequencies, aircraft deployment, and estimated load factors to model route profitability.
Corporate booking tools require direct fare access and rule extraction to enforce corporate travel policies.
Track schedule changes, delays, and cancellations to inform passengers and adjust downstream logistics.
Monitor Dream Miles redemption rates and availability to analyse loyalty program valuation.
"RwandAir's expansion through the Kigali hub creates critical pricing data for African aviation — but extracting it requires navigating complex booking engine states."
Airline booking engines are notoriously difficult to scrape. They rely on strict session tokens, heavy JavaScript rendering, and aggressive bot mitigation. DataFlirt manages the entire extraction lifecycle, delivering clean, normalised flight and pricing data directly to your warehouse so your team can focus on analysis.
Everything supported by our rwandair.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 rwandair.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing is generally permissible for commercial use. DataFlirt targets only public, non-authenticated data. We do not extract personal passenger data (PNR) or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to bypass standard WAF protections.
Yes. We can configure the pipeline to query specific multi-city routes, extracting the combined pricing and distinct leg schedules.
Pipelines can be configured to run daily, hourly, or on a continuous streaming basis depending on your specific latency requirements and the size of the route list.
Yes. Where the booking engine exposes it, we extract the base fare, total taxes, and the final total price, along with the currency code.
Our smallest packages start at a defined list of Origin-Destination pairs with daily delivery. For larger network tracking, we price based on volume and delivery frequency.
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 daily schedule dump or continuous price-monitoring across the network — we scope, build, and operate the pipeline. Tell us what you need.
