We extract flight schedules, dynamic pricing signals, seat availability, and route metadata from Air Mauritius. 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 airmauritius.com. All fields typed and schema-versioned.
"flight_number": "MK045", "origin": "MRU", "destination": "CDG", "departure_time": "2024-10-12T22:35:00Z", "arrival_time": "2024-10-13T08:15:00Z", "duration_minutes": 700, "aircraft_type": "Airbus A350-900", "stops": 0, "operated_by": "Air Mauritius"
| # | flight_number | origin | destination | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fare & Pricing objects from airmauritius.com. All fields typed and schema-versioned.
"flight_number": "MK045", "cabin_class": "Economy", "fare_basis": "YLE1MRU", "base_fare": 35000.0, "taxes": 8500.0, "total_fare": 43500.0, "currency": "MUR", "refundable": false, "change_fee": 5000.0
| # | flight_number | cabin_class | fare_basis | base_fare | taxes | total_fare |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Seat Availability objects from airmauritius.com. All fields typed and schema-versioned.
"flight_number": "MK045", "cabin_class": "Business", "seats_remaining": 4, "waitlist_status": "open", "pitch_inches": 44, "width_inches": 20, "power_outlets": true, "seat_map_url": "https://airmauritius.com/seatmap/MK045"
| # | flight_number | cabin_class | seats_remaining | waitlist_status | seat_map_url | pitch_inches |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Route Metadata objects from airmauritius.com. All fields typed and schema-versioned.
"origin_airport": "MRU", "dest_airport": "LHR", "distance_miles": 6065, "frequency_per_week": 7, "seasonal_route": false, "first_flight_date": "2024-01-01", "last_flight_date": "2024-12-31", "codeshare_partners": "['AF', 'MK']"
| # | origin_airport | dest_airport | distance_miles | frequency_per_week | seasonal_route | first_flight_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Baggage & Ancillaries objects from airmauritius.com. All fields typed and schema-versioned.
"flight_number": "MK045", "cabin_class": "Economy", "checked_bags_allowed": 2, "max_weight_kg": 23, "extra_bag_fee": 7500.0, "meal_included": true, "lounge_access": false, "priority_boarding": false
| # | flight_number | cabin_class | checked_bags_allowed | max_weight_kg | extra_bag_fee | meal_included |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Air Mauritius scraper handles the airline booking engine: route grids, dynamic fare pricing, availability triggers, and ancillary fees - with JavaScript rendering and session management built in.
Flight numbers, origins, destinations, departure and arrival times, and aircraft types scraped across the entire network.
Capture base fares, taxes, surcharges, and total prices across Economy and Business classes.
Extract remaining seat counts and waitlist statuses to gauge flight load factors and demand.
Extract baggage allowances, excess weight penalties, and optional service fees per fare class.
Map multi-leg journeys, layover durations, and codeshare partner operations.
Track miles required for reward flights and upgrade availability across routes.
Extract pricing in MUR, EUR, USD, GBP, and other supported currencies based on point of sale.
Identify aircraft types, seat pitch, layout, and inflight amenities for specific flights.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences.
Brief in. Clean data out.
Provide O&D pairs, dates, or flight numbers. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, session management, and rate-limit handling for airmauritius.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.
Airline booking engines use strict rate limiting and complex session states. Here is how we stay resilient.
Booking engines require strict sequential requests with valid session tokens. We maintain persistent cookie jars and valid CSRF tokens across multi-step search flows.
Airlines block high-frequency IP addresses. Our crawlers use residential proxies with realistic browser fingerprints and randomised request timing to avoid detection.
Flight results and dynamic pricing grids load via asynchronous API calls. We run full Playwright browser sessions to capture hydrated data that simple HTTP requests miss.
Booking engines update their UI frequently. Our selector strategy uses multiple fallback chains per field to ensure continuous data flow even when layouts change.
For large route networks, we maintain a hash index of last-seen fares. Subsequent runs only push diffs to reduce compute cost and downstream processing load.
Online travel agencies monitor direct channel fares to ensure parity and optimise markup strategies.
Rival airlines track Air Mauritius pricing on overlapping routes to adjust their own revenue management algorithms.
Analysts correlate seat availability drops with pricing surges to model route-level demand and load factors.
Metasearch engines integrate schedule and pricing data to provide comprehensive flight options to end users.
Enterprise travel managers audit fare histories to negotiate better corporate rates and track compliance.
Aviation consultants analyse frequency, aircraft deployment, and seasonal route changes to map regional connectivity.
"Airline pricing is highly dynamic, but extracting it requires navigating complex session states and strict rate limits on the booking engine."
Most teams underestimate the investment required: reliable airline scraping requires residential proxies, full JavaScript rendering for booking flows, session token management, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our airmauritius.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. Playwright handles JavaScript rendering and booking engine interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions for multi-step searches.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About airmauritius.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and fares is generally permissible. We do not extract personal passenger data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions, and request timing modelled on human behaviour to avoid triggering blocks.
Yes. We configure the crawler to select specific points of sale or currency toggles to extract pricing in MUR, EUR, USD, or other available currencies.
Pipelines can be configured for daily schedule refreshes or high-frequency intra-day fare checks for specific O&D pairs.
Yes. When the booking engine surfaces the fare breakdown, we extract base fare, taxes, carrier-imposed surcharges, and total price as separate fields.
Our smallest packages start at a defined list of O&D pairs with daily delivery. Contact us with your use case for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off schedule dump or a continuous fare-monitoring feed across the network - we scope, build, and operate the pipeline. Tell us what you need.
