We extract flight schedules, fare families, LifeMiles rates, and seat availability from Avianca. 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 avianca.com. All fields typed and schema-versioned.
"origin_iata": "BOG", "destination_iata": "MIA", "flight_number": "AV 6", "departure_time_utc": "2026-08-14T14:20:00Z", "duration_minutes": 225, "stop_count": 0
| # | flight_number | origin_iata | destination_iata | departure_time_utc | arrival_time_utc | aircraft_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Fares & Pricing objects from avianca.com. All fields typed and schema-versioned.
"fare_family": "classic", "base_fare": 245.5, "taxes_fees": 84.1, "total_price": 329.6, "currency": "USD", "point_of_sale": "US", "lifemiles_accrual": 1250
| # | flight_id | fare_family | base_fare | taxes_fees | total_price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for LifeMiles Redemption objects from avianca.com. All fields typed and schema-versioned.
"award_type": "economy", "miles_required": 15000, "cash_surcharge": 45.5, "currency": "USD", "availability_status": true, "cabin_class": "economy"
| # | flight_id | award_type | miles_required | cash_surcharge | currency | availability_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Seat Availability objects from avianca.com. All fields typed and schema-versioned.
"cabin_class": "business", "seats_available": 4, "layout_configuration": "1-2-1", "pitch_inches": 78, "premium_seat_fee": 0, "total_capacity": 28
| # | flight_id | cabin_class | total_capacity | seats_available | seat_map_url | pitch_inches |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Ancillary Services objects from avianca.com. All fields typed and schema-versioned.
"checked_bag_fee": 40.0, "carry_on_fee": 0.0, "seat_selection_fee": 15.0, "wifi_available": true, "meal_included": false, "pet_in_cabin_fee": 125.0
| # | flight_id | checked_bag_fee | carry_on_fee | seat_selection_fee | priority_boarding_fee | wifi_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Avianca scraper handles every layer of the booking platform: flight schedules, dynamic fare families, LifeMiles redemption rates, and seat map availability - with session management and anti-bot circumvention built in.
Extract origin, destination, departure, arrival, aircraft type, and duration for all Avianca routes.
Capture basic, classic, flex, and business fare tiers with associated baggage and flexibility rules.
Scrape prices based on specific POS locations to capture geographical pricing discrepancies.
Monitor miles required and cash surcharges for award flights across all cabins.
Extract available seats and cabin configurations to estimate load factors and yield.
Record dynamic pricing for checked bags, seat selection, and priority boarding.
Extract fares in COP, USD, EUR, and other local currencies, mapped to standard ISO codes.
Identify flights operated by Avianca Express, TACA, or Star Alliance partners.
Run pipelines at hourly intervals to capture flash sales and dynamic pricing shifts.
Brief in. Clean data out.
Provide route pairs, dates, and POS requirements. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for avianca.com.
Schema validation, null-rate checks, and fare-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket 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.
Airline sites employ aggressive bot protection. We use residential proxies and realistic TLS fingerprints to bypass WAF challenges without triggering blocks.
Fares are locked behind stateful search sessions. Our Playwright scripts maintain cookie jars and execute the exact multi-step XHR requests required to surface pricing.
Fares change based on the user's IP. We route requests through specific country proxies (Colombia, US, Spain) to extract accurate localised pricing.
Airlines cache availability heavily. We append cache-busting headers and manipulate search parameters to force real-time inventory responses from the backend.
Airline booking engines undergo frequent UI updates. We rely on underlying API responses and fallback DOM selectors to maintain pipeline stability.
Online travel agencies integrate direct Avianca schedules and fares to bypass expensive GDS distribution fees.
Rival airlines track Avianca pricing across LATAM routes to adjust their own revenue management algorithms.
Travel analytics platforms monitor LifeMiles redemption rates to identify high-value award availability.
Corporate travel managers monitor schedule changes, delays, and cancellations across the Avianca network.
Aviation analysts estimate flight profitability by tracking seat map availability over time.
Pricing teams analyse how Avianca adjusts fare families based on booking window and demand signals.
"Airline pricing is the ultimate dynamic dataset. Extracting Avianca fares at scale requires bypassing complex session states and aggressive bot mitigation."
Airlines do not want automated systems scraping their inventory. Reliable Avianca extraction requires managing stateful booking flows, defeating Akamai bot protection, and routing requests through specific geographic proxies to capture accurate Point of Sale pricing. DataFlirt handles this infrastructure natively.
Everything supported by our avianca.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 required for booking engines.
We maintain pools of residential ISP proxies across Colombia, US, and EU regions to ensure accurate Point of Sale pricing and bypass location-based restrictions.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About avianca.com scraping, legality, and pipeline operations.
Ask us directly →Scraping public flight schedules and fares is generally permissible under applicable law. DataFlirt targets only public, non-authenticated pricing and schedule data. We do not extract personal data or circumvent authentication walls.
We use residential proxies, realistic browser fingerprints, and request timing modelled on human behaviour to bypass Akamai and Datadome.
Yes. We route requests through specific geographic IP addresses to capture localised pricing in COP, USD, or EUR.
Yes. We extract the miles required, cash surcharges, and cabin availability for award flights across the network.
We can poll specific route pairs at hourly intervals to capture flash sales and dynamic pricing shifts.
Yes. We parse the seat map API responses to determine total capacity and currently available seats per cabin class.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off route schedule dump or a continuous fare-monitoring feed across 15,000 routes - we scope, build, and operate the pipeline. Tell us what you need.
