We extract flight schedules, dynamic fare pricing, seat availability, and Aeroplan redemption rates from Air Canada. 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 aircanada.com. All fields typed and schema-versioned.
"flight_number": "AC043", "origin": "DEL", "destination": "YYZ", "departure_time": "2024-10-12T22:50:00Z", "arrival_time": "2024-10-13T05:05:00Z", "duration": "15h 45m", "aircraft_type": "Boeing 777-200LR", "operating_carrier": "Air Canada"
| # | flight_number | origin | destination | departure_time | arrival_time | duration |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Fares objects from aircanada.com. All fields typed and schema-versioned.
"flight_number": "AC043", "date": "2024-10-12", "currency": "CAD", "standard_fare": 1452.0, "flex_fare": 1682.0, "premium_economy": 2845.0, "signature_class": 6450.0
| # | flight_number | date | currency | basic_fare | standard_fare | flex_fare |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Aeroplan Redemption objects from aircanada.com. All fields typed and schema-versioned.
"flight_number": "AC043", "points_required": 75000, "cash_surcharge": 124.5, "cabin_class": "Economy", "availability_status": "Available", "dynamic_pricing_flag": true, "redemption_category": "Standard"
| # | flight_number | points_required | cash_surcharge | cabin_class | availability_status | dynamic_pricing_flag |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Seat Availability objects from aircanada.com. All fields typed and schema-versioned.
"flight_number": "AC043", "cabin_class": "Signature Class", "total_seats": 40, "available_seats": 12, "occupied_seats": 28, "pitch_inches": 78, "layout": "1-2-1", "wifi_available": true
| # | flight_number | cabin_class | total_seats | available_seats | occupied_seats | seat_map_url |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Flight Status objects from aircanada.com. All fields typed and schema-versioned.
"flight_number": "AC043", "date": "2024-10-12", "scheduled_departure": "22:50", "estimated_departure": "23:15", "status_code": "Delayed", "gate": "14", "terminal": "3"
| # | flight_number | date | scheduled_departure | estimated_departure | scheduled_arrival | estimated_arrival |
|---|---|---|---|---|---|---|
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Our Air Canada scraper navigates complex booking flows, manages session tokens, and bypasses Akamai bot protection to deliver accurate fare matrices and schedule data.
Extract origin, destination, departure, arrival, duration, and aircraft type for any route network.
Capture Basic, Standard, Flex, Comfort, Latitude, Premium Economy, and Signature Class pricing.
Extract points pricing and cash surcharges for reward bookings across all cabin classes.
Monitor available versus occupied seats per cabin to estimate load factors and demand.
Track delays, cancellations, gate changes, and terminal assignments in real time.
Extract connection times, operating carriers, and layover details for complex routes.
Capture checked bag costs, seat selection fees, and Wi-Fi pricing per flight segment.
Log equipment types, cabin layouts, and onboard amenities for competitive benchmarking.
Run continuous pipelines with change-detection to emit only updated fares and schedules.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, or flight numbers. We design the extraction schema.
We configure Scrapy and Playwright crawlers with Akamai-resistant proxies and session management.
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.
Airlines employ aggressive bot mitigation and complex session management. Here is how we maintain stable extraction.
Air Canada uses strict Akamai bot protection. Our crawlers use residential ISP proxies and TLS fingerprint spoofing to maintain high success rates and avoid IP bans.
Flight searches require maintaining complex session states and validation tokens. We manage cookie jars across multi-step booking flows to ensure valid fare returns.
Fare matrices and seat maps are heavily JavaScript-rendered. We execute full browser sessions to hydrate data that standard HTTP requests miss.
We maintain hash indexes of fare prices. Subsequent runs emit diffs, reducing downstream processing load and storage costs.
Every run emits structured logs to our observability stack. We alert on null-rate spikes or schema drift immediately.
OTAs and competing airlines monitor fare changes across cabin classes to adjust their own pricing models.
Credit card companies and travel aggregators track Aeroplan point valuations and redemption availability.
Analysts use seat map availability and load factors to predict route profitability and passenger volumes.
Travel risk platforms ingest real-time flight status and delay data to automate rebooking workflows.
Enterprise procurement teams audit negotiated corporate rates against public Air Canada pricing.
Aviation consultants analyse schedule frequencies and aircraft deployments for market research.
"Air Canada's dynamic pricing and Aeroplan valuations change constantly. Querying this data at scale requires a managed pipeline, not a basic HTTP client."
Airlines invest heavily in bot protection. Reliable Air Canada scraping requires residential proxies, full JavaScript rendering for fare matrices, and session token management. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our aircanada.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.
Handles crawl orchestration and JavaScript rendering for complex flight booking flows and fare matrices.
ISP-grade proxies bypass Akamai and Datadome protections on airline sites to maintain high extraction success rates.
Pipelines run on AWS Lambda and ECS with Airflow scheduling and PostgreSQL state management.
Data delivered to where your team already works — no new tooling required.
About aircanada.com scraping, legality, and pipeline operations.
Ask us directly →Scraping public flight schedules and fares is generally permissible. We do not extract PII or bypass authenticated passenger portals.
We use residential ISP proxies and realistic TLS fingerprints to bypass WAF rules and avoid IP blocks.
Yes, we extract point requirements and cash surcharges for all cabin classes.
Pipelines can run at hourly cadences for high-priority routes, capturing dynamic fare changes.
Yes, we parse seat maps to calculate occupied versus available seats per flight.
We start with defined origin-destination pairs and date ranges. Contact us for volume pricing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need daily schedule updates or continuous fare monitoring — we scope, build, and operate the pipeline. Tell us what you need.
