We extract flight schedules, multi-tier pricing, seat availability, and ancillary fees from Vivaaerobus. 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 vivaaerobus.com. All fields typed and schema-versioned.
"flight_number": "VB3124", "origin_iata": "MEX", "destination_iata": "CUN", "departure_time_local": "2026-11-14T08:30:00", "arrival_time_local": "2026-11-14T11:45:00", "duration_minutes": 135, "stops_count": 0, "aircraft_type": "Airbus A320"
| # | flight_number | origin_iata | destination_iata | departure_time_local | arrival_time_local | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fare Tiers objects from vivaaerobus.com. All fields typed and schema-versioned.
"flight_number": "VB3124", "fare_tier_name": "Smart", "base_fare": 1250.0, "taxes_fees": 580.0, "total_price": 1830.0, "currency": "MXN", "cabin_bag_included": true, "checked_bag_included": true
| # | flight_number | fare_tier_name | base_fare | taxes_fees | total_price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ancillary Fees objects from vivaaerobus.com. All fields typed and schema-versioned.
"flight_number": "VB3124", "seat_selection_min": 150.0, "seat_selection_max": 450.0, "checked_bag_fee": 600.0, "priority_boarding_fee": 200.0, "pet_in_cabin_fee": 1200.0, "currency": "MXN"
| # | flight_number | route_id | seat_selection_min | seat_selection_max | carry_on_bag_fee | checked_bag_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Promotions & Discounts objects from vivaaerobus.com. All fields typed and schema-versioned.
"route_id": "MEX-CUN", "promo_code_active": "VIVA20", "discount_percentage": 20, "viva_fan_price": 1450.0, "standard_price": 1830.0, "blackout_dates_apply": true, "currency": "MXN"
| # | route_id | promo_code_active | discount_percentage | viva_fan_price | standard_price | valid_from |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Matrix objects from vivaaerobus.com. All fields typed and schema-versioned.
"origin_airport": "MEX", "destination_airport": "CUN", "min_price_monthly": 890.0, "direct_flight": true, "days_operated": "['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']", "distance_km": 1295, "last_updated": "2026-10-01T14:22:00Z"
| # | origin_airport | destination_airport | available_dates | min_price_monthly | direct_flight | days_operated |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Low-cost carriers use session profiling to inflate prices. Our infrastructure bypasses these traps, delivering un-cached fare data across all tiers and ancillary options.
Capture departure times, arrival times, aircraft types, and flight durations for all active Vivaaerobus routes.
Extract accurate pricing for Zero, Light, Extra, and Smart fare tiers simultaneously, including base fare and tax breakdowns.
Monitor dynamic fees for checked baggage, carry-on limits, seat selection, and priority boarding per route.
Extract the 30-day low-fare calendar view to identify pricing trends and promotional windows quickly.
Extract fares in MXN, USD, or COP, maintaining accurate conversion rates as displayed by the airline.
Rotate proxies and clear cookies between requests to prevent the airline from artificially inflating prices based on search history.
Execute complex JavaScript required to load dynamic pricing elements on the Vivaaerobus booking engine.
Run continuous pipelines and only receive records when a flight's price or availability changes.
Capture the member-exclusive Viva Fan pricing alongside standard fares to measure loyalty program value.
Handle Akamai and Cloudflare protections automatically using residential proxies and human-like interaction patterns.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and desired currencies. We design the extraction schema together.
We configure Playwright crawlers, residential proxy rotation, and session management tailored to the Vivaaerobus booking engine.
Schema validation, null-rate checks, and price-accuracy verification against manual searches before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines aggressively block automated searches to protect their pricing models. Here is how we maintain reliable extraction.
Airlines track search frequency via cookies and IP to inflate prices on highly demanded routes. We isolate every search into a clean browser context with a fresh IP, ensuring you extract the baseline fare.
The Vivaaerobus search results are entirely client-side rendered. We use Playwright to execute the JavaScript, wait for API hydration, and extract the structured fare data from the DOM.
High-frequency flight searches trigger edge firewall blocks. We route requests through ISP-grade residential proxies in Mexico and the US to blend in with legitimate consumer traffic.
Airlines frequently rename fare tiers or alter the layout of ancillary options. Our extraction logic uses multiple fallback selectors to ensure data integrity even when the UI changes.
Sometimes the airline's backend fails to return prices for specific dates. We detect these anomalies, automatically retry the search, and alert on persistent availability issues.
Online travel agencies ingest raw schedule and pricing data to offer comprehensive flight options to their users.
Rival airlines monitor Vivaaerobus pricing across shared routes to adjust their own promotional strategies and fare tiers.
Aviation analysts track flight frequencies and fare fluctuations to estimate load factors and route profitability.
Revenue management teams use historical fare data to train machine learning models for their own dynamic pricing algorithms.
Insurance and logistics companies monitor flight status changes and schedule adjustments for operational planning.
Travel deal platforms track the 30-day calendar matrix to alert subscribers when fares drop below historical averages.
"Vivaaerobus dynamically adjusts fares across four distinct tiers and dozens of add-ons based on session data, making extraction complex."
Most teams fail at scraping low-cost carriers because they ignore session-based price inflation and JavaScript rendering requirements. DataFlirt manages the residential proxies and Playwright execution needed to extract accurate, un-cached fare data across all Vivaaerobus routes.
Everything supported by our vivaaerobus.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 manages the crawl queue and retry logic, while Playwright handles the complex JavaScript rendering required by the airline booking engine.
We route traffic through high-quality residential proxies in Mexico and the US to avoid edge blocking and ensure accurate regional pricing.
Pipelines run on Kubernetes with Airflow scheduling, allowing us to scale up instantly for high-frequency price polling.
Data delivered to where your team already works — no new tooling required.
About vivaaerobus.com scraping, legality, and pipeline operations.
Ask us directly →Airlines use strict edge protection like Akamai or Cloudflare. We bypass these using ISP-grade residential proxies, realistic browser fingerprints via Playwright, and human-like interaction patterns during the search flow.
Yes. A single search query extracts the pricing for Zero, Light, Extra, and Smart tiers, along with their respective baggage allowances and cancellation policies.
We isolate every search in a clean browser context. Cookies, local storage, and session data are cleared, and a new proxy IP is assigned to ensure the airline returns the baseline un-cached fare.
Depending on your target route volume, we can configure pipelines to poll prices daily, hourly, or at custom intervals required for your dynamic pricing models.
Yes. We navigate the booking flow to extract the dynamic costs associated with seat selection, checked baggage, and priority boarding for specific flights.
Engagements typically start with a defined list of origin-destination pairs and a set polling frequency. Contact us with your route list for a specific quote.
Yes. We offer a sample extraction of up to 20 routes over a 7-day departure window so you can validate the schema and data accuracy before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need daily schedule updates or high-frequency price tracking across thousands of routes, we build and operate the infrastructure. Tell us what you need.
