We extract flight schedules, dynamic pricing matrices, fare family rules, and Enrich points availability from malaysiaairlines.com. 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 malaysiaairlines.com. All fields typed and schema-versioned.
"flight_number": "MH2", "origin": "KUL", "destination": "LHR", "departure_time": "2026-08-14T23:30:00Z", "arrival_time": "2026-08-15T05:55:00Z", "duration": "13h 25m", "aircraft_type": "Airbus A350-900", "operating_airline": "Malaysia Airlines"
| # | flight_number | origin | destination | departure_time | arrival_time | duration |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Fares objects from malaysiaairlines.com. All fields typed and schema-versioned.
"flight_number": "MH2", "currency": "MYR", "lite_fare": 2450.0, "basic_fare": 2850.0, "flex_fare": 3450.0, "business_promotional": 8900.0, "business_flex": 12500.0, "taxes": 450.0
| # | flight_number | currency | lite_fare | basic_fare | flex_fare | business_promotional |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Enrich Redemption objects from malaysiaairlines.com. All fields typed and schema-versioned.
"flight_number": "MH2", "origin": "KUL", "destination": "LHR", "miles_required": 45000, "cash_surcharge": 850.0, "cabin_class": "Economy", "availability_status": "Available", "promotion_applied": false
| # | flight_number | origin | destination | miles_required | cash_surcharge | cabin_class |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Information objects from malaysiaairlines.com. All fields typed and schema-versioned.
"origin_code": "KUL", "destination_code": "LHR", "frequency": "Daily", "seasonality": "Year-round", "distance": "10530 km", "terminal": "Terminal 1", "connection_airport": "None", "connection_time": "None"
| # | origin_code | destination_code | frequency | seasonality | distance | connection_airport |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Ancillary & Baggage objects from malaysiaairlines.com. All fields typed and schema-versioned.
"flight_number": "MH2", "cabin_class": "Economy Basic", "check_in_allowance": "20kg", "cabin_allowance": "7kg", "extra_baggage_fee": 150.0, "seat_selection_fee": 45.0, "lounge_access_fee": 250.0, "meal_included": true
| # | flight_number | cabin_class | check_in_allowance | cabin_allowance | extra_baggage_fee | seat_selection_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our scraper navigates the complex booking engine backend, handling session tokens, dynamic pricing matrices, and multi-leg itineraries with JavaScript rendering and anti-bot circumvention built in.
Extract departure times, arrival times, aircraft types, and stopover durations across the entire global network.
Capture base fares, taxes, and total prices across Lite, Basic, Flex, and Business cabin classes.
Monitor points required for redemption flights, including cash surcharges and availability status.
Extract connection times, layover details, and terminal changes for complex multi-leg journeys.
Track discounted student travel program rates and specific baggage allowances tied to MHexplorer.
Capture check-in baggage allowances, extra weight fees, seat selection costs, and Golden Lounge access rates.
Identify operating airlines for Oneworld partner flights booked through the Malaysia Airlines portal.
Extract pricing in MYR, USD, GBP, AUD, and other supported currencies based on point of sale.
Run one-off bulk exports or configure continuous pipelines at hourly cadences with change-detection diffing.
Brief in. Clean data out.
Provide origin and destination pairs, dates, and cabin classes. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for the booking engine.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airline booking engines use strict rate limiting and complex state management. Here is how we stay resilient.
Airline pricing engines require maintaining stateful sessions across multiple requests. We manage cookies, search tokens, and session headers to ensure accurate fare matrices without triggering timeouts.
Travel sites deploy aggressive bot mitigation. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to avoid IP blocks and CAPTCHA loops.
Fare matrices and availability calendars are entirely JavaScript-rendered. We run full Playwright browser sessions to execute scripts and capture data that headless HTTP clients miss.
For large route networks, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, and coverage drops, responding before you notice.
Travel aggregators monitor direct-channel pricing to ensure parity and update cached search results.
Rival airlines track fare changes on overlapping routes to adjust their own dynamic pricing models.
Analysts monitor fare bucket availability and pricing curves to optimise yield management strategies.
Data firms analyze route frequency, aircraft deployment, and capacity trends across the Southeast Asian market.
Points aggregators track Enrich redemption rates to alert users to high-value arbitrage opportunities.
Enterprise booking tools ingest schedules and base fares to enforce policy compliance and manage budgets.
"Malaysia Airlines pricing data is locked behind complex session states and backend APIs, rendering standard HTTP clients useless."
Most teams underestimate the investment required: reliable airline scraping requires residential proxies, full JavaScript rendering for booking flows, stateful session handling, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our malaysiaairlines.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 regional endpoints. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda for burst tasks and ECS for sustained loads. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About malaysiaairlines.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and schedule information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated flight data. We do not extract personal passenger data, circumvent authentication walls for user profiles, or violate data protection regulations. Clients should review airline terms of service and consult legal counsel for specific use cases.
Airline booking engines drop sessions quickly to free up inventory. We use stateful session management, passing search tokens and cookies efficiently through our Playwright instances, ensuring the pricing matrix is captured before the session expires.
Yes. We can configure the pipeline to query routes specifically under the MHexplorer parameters, extracting the discounted base fares and modified baggage allowances associated with the program.
Real-time streaming pipelines achieve sub-30-minute latency for price signals on a defined route set. Full network refreshes complete within a 4-8 hour window depending on query depth and date ranges.
Yes. We extract the required Enrich points, the mandatory cash surcharges, and the availability status for award flights across all cabin classes.
Our smallest packages start at a defined list of origin and destination pairs with daily delivery. For larger networks covering thousands of routes, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 50 routes across multiple date ranges as part of the pre-engagement scoping process, so you can validate schema fit and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off route schedule dump or a continuous price-monitoring feed across 10,000 origin and destination pairs, we scope, build, and operate the pipeline. Tell us what you need.
