We extract flight schedules, dynamic pricing tiers, seat availability, and route networks from copaair.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 copaair.com. All fields typed and schema-versioned.
"origin": "PTY", "destination": "MIA", "departure_time": "2026-10-14T08:30:00Z", "arrival_time": "2026-10-14T12:45:00Z", "flight_number": "CM 430", "aircraft_type": "Boeing 737 MAX 9"
| # | origin | destination | departure_time | arrival_time | flight_number | aircraft_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Fares objects from copaair.com. All fields typed and schema-versioned.
"flight_number": "CM 430", "date": "2026-10-14", "fare_class_basic": 245.5, "fare_class_classic": 295.5, "currency": "USD", "taxes": 45.2
| # | flight_number | date | fare_class_basic | fare_class_classic | fare_class_full | fare_class_business |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seat Availability objects from copaair.com. All fields typed and schema-versioned.
"flight_number": "CM 430", "date": "2026-10-14", "cabin_class": "Economy", "seats_remaining": 4, "exit_row_available": true, "upgrade_eligible": true
| # | flight_number | date | cabin_class | seats_remaining | seat_map_url | exit_row_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Route Network objects from copaair.com. All fields typed and schema-versioned.
"origin_airport": "EZE", "destination_airport": "JFK", "connection_hub": "PTY", "operating_carrier": "Copa Airlines", "codeshare": false, "seasonal": false
| # | origin_airport | destination_airport | distance_miles | frequency_weekly | connection_hub | operating_carrier |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for ConnectMiles objects from copaair.com. All fields typed and schema-versioned.
"route": "PTY-LAX", "date": "2026-11-05", "cabin": "Business", "miles_required": 45000, "taxes_usd": 55.0, "award_type": "Saver"
| # | route | date | cabin | miles_required | taxes_usd | availability_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our pipeline handles every layer of the booking platform: flight schedules, dynamic fare classes, seat availability, and ConnectMiles award pricing, with session management and anti-bot circumvention built in.
Origin, destination, departure times, arrival times, aircraft type, and stopover duration extracted at the route level.
Capture pricing for Basic, Classic, Full, and Business classes, including tax breakdowns and currency conversions.
Extract mileage requirements, Saver vs Standard award availability, and associated taxes for loyalty program analysis.
Track remaining seats per cabin class and monitor exit row or premium seat upgrade inventory.
Parse complex itineraries connecting through the Hub of the Americas (PTY) to North, Central, and South America.
Extract baggage allowance rules, overweight fees, and seat selection costs associated with each fare class.
Identify operating aircraft models, codeshare agreements, and operating carrier details for every flight segment.
Extract pricing in USD, COP, BRL, ARS, and other regional currencies supported by the booking engine.
Run one-off bulk 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, or cabin classes. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for copaair.com.
Schema validation, null-rate checks, price-outlier detection, and sample routes before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines invest heavily in scraping detection. Here is how we stay resilient, and why teams choose managed infrastructure over DIY scripts.
Airline booking engines use strict bot protection based on IP reputation and browser fingerprints. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management.
Flight search results are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution and lazy-load triggering, capturing data that headless HTTP clients miss entirely.
Airline sites expire search sessions quickly. We maintain active session states, handle token refreshes automatically, and gracefully retry expired searches without dropping data.
For large route catalogues, 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.
Other airlines and travel agencies monitor Copaair pricing tiers and promotional fares to adjust their own revenue management strategies.
Online travel agencies ingest direct schedule and pricing data to supplement GDS feeds and verify seat availability.
Aviation analysts track flight frequencies, aircraft deployment, and pricing trends across the Hub of the Americas network.
Points and miles aggregators track ConnectMiles award availability and redemption rates for their user bases.
Risk management platforms monitor schedule changes, cancellations, and re-routings for corporate travel alerts.
Enterprise travel desks integrate direct pricing feeds to audit their negotiated corporate rates against public fares.
"Airline pricing is the original dynamic market. Capturing real-time fare changes across the Hub of the Americas requires infrastructure, not just a script."
Most teams underestimate the investment required: reliable airline scraping requires residential proxies, full JavaScript rendering for SPA searches, session state management, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our copaair.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 for the booking engine.
We maintain pools of residential ISP proxies across LATAM and US regions. Rotation happens per search request with sticky sessions to maintain booking state.
Pipelines run on AWS Lambda for burst scaling and ECS for sustained loads. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About copaair.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing data 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, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate limits in real time and trigger pool rotation automatically.
Yes. We extract public award availability, mileage requirements, and associated taxes for ConnectMiles redemptions on specific routes and dates.
Real-time streaming pipelines achieve sub-60-minute latency for price signals on a defined route set. Full catalogue refreshes at daily cadence complete within a 4-8 hour window depending on size.
Yes. We can configure searches for multi-city routes, extracting the full itinerary segments, connection times at PTY, and total bundled pricing.
Our smallest packages start at a defined route list, typically 500 to 5,000 origin-destination pairs, with daily delivery. For larger catalogues, we price based on search volume and delivery frequency.
Yes. We provide a sample run of up to 100 routes as part of the pre-engagement scoping process, allowing you to validate schema fit, field completeness, and data quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily route catalogue dump or a continuous price-monitoring feed across 5,000 routes, we scope, build, and operate the pipeline. Tell us what you need.
