We extract route schedules, dynamic fare grids, KrisFlyer redemption rates, and cabin availability from singaporeair.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 singaporeair.com. All fields typed and schema-versioned.
"flight_number": "SQ322", "origin_iata": "SIN", "destination_iata": "LHR", "departure_time_local": "2026-10-14T23:30:00", "arrival_time_local": "2026-10-15T05:55:00", "duration_minutes": 865, "aircraft_type": "Airbus A380-800", "operated_by": "Singapore 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 Fare Pricing objects from singaporeair.com. All fields typed and schema-versioned.
"flight_number": "SQ322", "cabin_class": "Business", "fare_family": "Business Advantage", "base_fare": 4850.0, "taxes_fees": 312.4, "total_price": 5162.4, "currency": "SGD", "baggage_allowance_kg": 40
| # | flight_number | cabin_class | fare_family | base_fare | taxes_fees | total_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for KrisFlyer Awards objects from singaporeair.com. All fields typed and schema-versioned.
"flight_number": "SQ322", "cabin_class": "Business", "award_type": "Saver", "miles_required": 103500, "taxes_fees": 65.2, "currency": "SGD", "waitlist_status": true, "spontaneous_escapes_promo": false
| # | flight_number | cabin_class | award_type | miles_required | taxes_fees | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Route Network objects from singaporeair.com. All fields typed and schema-versioned.
"origin_code": "SIN", "destination_code": "LHR", "frequency_per_week": 28, "direct_flight": true, "distance_km": 10889, "flight_numbers_list": "['SQ308', 'SQ318', 'SQ322', 'SQ312']"
| # | origin_code | destination_code | frequency_per_week | direct_flight | distance_km | seasonality_start |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Fare Conditions objects from singaporeair.com. All fields typed and schema-versioned.
"fare_basis_code": "D14R", "cancellation_fee": 300.0, "noshow_fee": 500.0, "upgrade_eligible": true, "mileage_accrual_pct": 125, "stopover_permitted": true, "lounge_access": true
| # | fare_basis_code | min_stay_days | max_stay_days | cancellation_fee | noshow_fee | upgrade_eligible |
|---|---|---|---|---|---|---|
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Our singaporeair.com scraper handles strict session states, AJAX fare grids, and Akamai bot mitigation to deliver accurate pricing and inventory data across all cabin classes.
Capture flight numbers, departure and arrival times, aircraft types, and operator details across the entire Singapore Airlines and Scoot network.
Extract base fares, taxes, and total prices for Economy, Premium Economy, Business, First, and Suites across all fare families.
Track Saver and Advantage award inventory, required miles, waitlist status, and applicable taxes for redemption bookings.
Monitor seat availability thresholds and fare class buckets to understand yield management patterns.
Automatically detect and extract discounted award flights during monthly Spontaneous Escapes promotional windows.
Extract fares localised to point-of-sale currencies, bypassing default IP-based geolocation routing.
Identify true operators for Star Alliance and partner flights sold through the singaporeair.com booking engine.
Extract cancellation fees, no-show penalties, mileage accrual rates, and upgrade eligibility for specific fare basis codes.
Configure continuous pipelines at hourly or daily cadences to track fare volatility on high-yield routes.
Bypass Akamai bot protection using residential proxies and precise TLS fingerprinting to maintain session persistence.
Brief in. Clean data out.
Provide origin-destination pairs, travel date ranges, and target cabin classes. We design the extraction schema.
We configure Scrapy and Playwright crawlers, proxy rotation, session management, and Akamai bypass for singaporeair.com.
Schema validation, null-rate checks, fare-outlier detection, and session stability testing before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Singapore Airlines uses aggressive bot mitigation and complex session management. Here is how we maintain data flow.
Singapore Airlines protects its booking engine with Akamai. We use residential ISP proxies combined with precise TLS fingerprinting and HTTP/2 header ordering to appear as legitimate passenger traffic.
Fare searches require stateful POST requests and valid session tokens. Our infrastructure maintains cookie jars and CSRF tokens across paginated date grids to prevent mid-search timeouts.
The flexible date search matrix loads via asynchronous JavaScript. We deploy Playwright to execute these scripts and wait for network idle states, ensuring complete fare extraction.
For large route networks, we maintain a hash index of last-seen fares. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs. We alert on null-rate spikes, currency anomalies, and coverage drops. SLA uptime is contractual.
Online travel agencies monitor direct-channel pricing to adjust markups and ensure parity compliance.
Points and miles platforms ingest KrisFlyer availability to alert users when highly coveted Suites or First Class inventory opens.
Competing airlines track fare family distribution and seat availability to benchmark yield on overlapping routes.
Travel management companies audit corporate negotiated rates against public web fares to ensure contract compliance.
Aviation analysts track schedule changes, frequency adjustments, and aircraft downgauges to monitor capacity shifts.
Hedge funds and institutional investors aggregate pricing volatility to model forward-looking revenue projections.
"Singapore Airlines operates one of the most complex pricing and award inventory systems in aviation, demanding precise session management to extract."
Most teams fail at airline scraping due to strict session timeouts, Akamai bot mitigation, and complex AJAX-driven fare matrices. DataFlirt handles the proxy rotation, TLS fingerprinting, and payload reverse-engineering so your engineers can focus on yield analysis rather than pipeline maintenance.
Everything supported by our singaporeair.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 and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows required for booking engines.
We maintain pools of residential ISP proxies to bypass Akamai bot mitigation. Rotation happens per-session to maintain stateful booking flows.
Pipelines run on AWS Lambda and ECS. 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 singaporeair.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing is generally permissible. DataFlirt targets only public, non-authenticated route and fare data. We do not extract personal passenger data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
We use residential ISP proxies combined with precise TLS fingerprinting, HTTP/2 header ordering, and request timing modelled on human behaviour. This ensures our requests are treated as legitimate passenger traffic.
Yes. We extract Saver and Advantage award tiers, required miles, waitlist status, and applicable taxes across all cabin classes.
Real-time streaming pipelines achieve sub-30-minute latency for fare changes on a defined route set. Full network refreshes complete within a 4-8 hour window depending on volume.
Yes. We configure specific pipelines to monitor and extract discounted award inventory during the monthly Spontaneous Escapes promotional windows.
Our smallest packages start at a defined route list (typically 100-500 origin-destination pairs) with daily delivery. For full network monitoring, we price based on volume and frequency.
Yes. We provide a sample run of up to 20 routes across multiple dates 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 one-off schedule dump or continuous fare monitoring across the global network, we scope, build, and operate the pipeline. Tell us what you need.
