We extract flight routes, hotel listings, dynamic pricing signals, and Go Prime membership rates from Govoyages. 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 Flights objects from govoyages.com. All fields typed and schema-versioned.
"route_id": "CDG-BCN-AF1024", "origin_airport": "CDG", "destination_airport": "BCN", "carrier_name": "Air France", "price_standard": 145.5, "price_prime": 95.0, "currency": "EUR", "stopover_count": 0
| # | route_id | origin_airport | destination_airport | departure_time | arrival_time | carrier_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Hotels objects from govoyages.com. All fields typed and schema-versioned.
"hotel_id": "HTL-98234", "property_name": "Hotel Arts Barcelona", "location_city": "Barcelona", "star_rating": 5, "price_per_night": 320.0, "prime_price_per_night": 285.0, "review_score": 8.9, "availability_status": "AVAILABLE"
| # | hotel_id | property_name | location_city | star_rating | price_per_night | prime_price_per_night |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Car Rentals objects from govoyages.com. All fields typed and schema-versioned.
"rental_id": "CAR-5521", "provider_name": "Europcar", "car_category": "Compact", "pickup_location": "BCN Airport", "price_total": 112.5, "prime_discount_applied": true, "transmission_type": "Automatic", "seat_count": 5
| # | rental_id | provider_name | car_category | pickup_location | dropoff_location | price_total |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Go Prime Pricing objects from govoyages.com. All fields typed and schema-versioned.
"item_id": "CDG-BCN-AF1024", "item_type": "FLIGHT", "standard_fare": 145.5, "prime_fare": 95.0, "discount_absolute": 50.5, "discount_percentage": 34.7, "trial_available": true, "currency": "EUR"
| # | item_id | item_type | standard_fare | prime_fare | discount_absolute | discount_percentage |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from govoyages.com. All fields typed and schema-versioned.
"search_id": "SRCH-8839201", "origin": "PAR", "destination": "BCN", "result_position": 1, "provider": "Vueling", "base_price": 45.0, "tax_amount": 22.5, "total_price": 67.5
| # | search_id | origin | destination | travel_date_start | travel_date_end | result_position |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Govoyages scraper handles every layer of the platform: flight itineraries, dynamic pricing, Go Prime member rates, and hotel inventory. We manage JavaScript rendering, session state, and anti-bot circumvention.
Extract departure times, arrival times, carriers, flight numbers, layovers, and total duration for any origin-destination pair.
Capture standard fares alongside Go Prime member prices to calculate exact discount margins across flights and hotels.
Extract property details, room types, nightly rates, star ratings, and review scores across global destinations.
Monitor fare fluctuations over time. We track base prices, taxes, and total costs timestamped per crawl.
Extract baggage fees, seat selection costs, and insurance add-ons that inflate the final ticket price.
Use residential proxies from specific European or global regions to capture IP-dependent pricing variations.
Extract rental providers, vehicle categories, pickup locations, and total costs for complete itinerary mapping.
Run pipelines at hourly or daily cadences to catch short-lived fare drops and flash sales.
Maintain a hash index of last-seen fares. Subsequent runs only push diffs, reducing downstream processing load.
Brief in. Clean data out.
Provide origin-destination pairs, travel dates, or hotel locations. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, session management, and CAPTCHA handling.
Schema validation, null-rate checks, price-outlier detection, and sample data review before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
OTAs invest heavily in scraping detection. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Govoyages uses aggressive bot protection. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Search results and dynamic pricing widgets are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution to capture data that headless HTTP clients miss.
OTAs alter prices based on search history and cookies. We isolate every search in a clean browser context to ensure you receive baseline prices, not inflated retargeting rates.
DOM structures change frequently. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline overnight.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, and coverage drops. SLA uptime is contractual.
Competing OTAs and airlines monitor Govoyages to ensure price parity and adjust their own dynamic pricing algorithms.
Analysts track Go Prime discount depths across routes to evaluate the unit economics of travel subscription models.
Travel aggregators map available routes, layover durations, and carrier dominance to optimise their own search offerings.
Research firms track average fare movements across European corridors to identify macroeconomic travel trends.
Revenue management teams correlate fare increases and hotel availability drops with seasonal demand spikes.
Machine learning teams use historical fare datasets to train predictive pricing models and recommendation engines.
"Govoyages holds critical European travel pricing data, but extracting it requires navigating aggressive bot protection and session-dependent fare volatility."
Most teams underestimate the investment required: reliable Govoyages scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our govoyages.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.
We maintain pools of residential ISP proxies across EU regions. Rotation happens per-request with clean sessions to avoid retargeting price inflation.
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 govoyages.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and route information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated flight, hotel, and pricing data. We do not extract personal data or bypass secure payment gateways.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for 403/CAPTCHA rate spikes in real time and trigger solver queues automatically.
Yes. Our pipeline extracts both the standard fare and the Go Prime discounted fare for every flight and hotel listing, allowing you to calculate exact discount margins.
OTAs often increase prices if the same route is searched repeatedly. We isolate every search request in a clean browser context with fresh cookies and rotated IPs to ensure you receive baseline pricing.
Real-time streaming pipelines achieve sub-60-minute latency for fare updates on a defined route set. Full catalogue refreshes operate on daily or hourly cadences depending on your requirements.
Our smallest packages start at a defined route list, typically 1,000 to 10,000 origin-destination pairs, with daily delivery. Contact us with your use case for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off route dump or a continuous price-monitoring feed across 50,000 itineraries, we scope, build, and operate the pipeline. Tell us what you need.
