We extract AVE, Avlo, and regional train schedules, dynamic fare pricing, and seat availability from Renfe. 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 Train Schedules objects from renfe.com. All fields typed and schema-versioned.
"train_id": "03092", "train_type": "AVE", "origin_station": "Madrid-Puerta de Atocha", "destination_station": "Barcelona-Sants", "departure_time": "2026-10-14T09:30:00Z", "arrival_time": "2026-10-14T12:00:00Z", "duration_minutes": 150, "status": "ON_TIME"
| # | train_id | train_type | origin_station | destination_station | departure_time | arrival_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ticket Pricing objects from renfe.com. All fields typed and schema-versioned.
"train_id": "03092", "fare_class": "Elige", "price": 64.5, "currency": "EUR", "availability_status": "AVAILABLE", "refundable": true, "exchangeable": true, "scraped_at": "2026-10-01T08:15:22Z"
| # | train_id | fare_class | price | currency | availability_status | refundable |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Data objects from renfe.com. All fields typed and schema-versioned.
"route_id": "MAD-BCN", "origin_city": "Madrid", "destination_city": "Barcelona", "distance_km": 504, "average_duration": 165, "daily_frequencies": 24, "active_route": true, "primary_corridor": "Northeast"
| # | route_id | origin_city | destination_city | distance_km | average_duration | train_types_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Station Metadata objects from renfe.com. All fields typed and schema-versioned.
"station_code": "60000", "station_name": "Madrid-Puerta de Atocha", "city": "Madrid", "region": "Madrid", "latitude": 40.4065, "longitude": -3.6896, "wheelchair_accessible": true, "connection_types": "['AVE', 'Alvia', 'Cercanías']"
| # | station_code | station_name | city | region | latitude | longitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Multi-Leg Journeys objects from renfe.com. All fields typed and schema-versioned.
"journey_id": "J-847291", "origin": "Sevilla-Santa Justa", "destination": "Girona", "total_duration": 345, "leg_count": 2, "transfer_stations": "['Madrid-Puerta de Atocha']", "total_price": 112.4, "currency": "EUR"
| # | journey_id | origin | destination | total_duration | leg_count | transfer_stations |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Renfe scraper handles complex search forms, session tokens, and dynamic pricing across all Spanish rail networks — from high-speed AVE to local Cercanías.
Extract high-speed rail schedules and low-cost alternatives with exact departure and arrival timestamps.
Capture Básico, Elige, and Prémium pricing tiers as they fluctuate based on demand and booking windows.
Track real-time capacity signals and sold-out statuses across different cabin classes.
Map complex routes requiring transfers, including wait times and station change logistics.
Gather geospatial coordinates, accessibility features, and facility information for every station.
Build time-series datasets for fare fluctuations to power predictive pricing models.
Access regional and commuter train timetables for localized mobility analysis.
Monitor train status and estimated arrival times for operational intelligence.
Test fare changes against standard discounts like Tarjeta Dorada or Carné Joven.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences.
Brief in. Clean data out.
Provide origin-destination pairs, travel dates, or station codes. We design the extraction schema together.
We configure Playwright crawlers, Spanish proxy rotation, session management, and search form navigation for renfe.com.
Schema validation, null-rate checks, price-outlier detection, and route verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Renfe employs strict session management and rate limiting. Here is how we maintain steady extraction.
Renfe search forms require valid session tokens generated via complex JavaScript flows. We manage the entire token lifecycle, refreshing sessions before they expire to ensure uninterrupted data extraction.
Accessing Renfe from non-Spanish IP ranges frequently triggers aggressive blocking. We route all requests through high-quality Spanish residential proxies to maintain a natural geographic profile.
The Renfe booking engine relies heavily on client-side rendering. We execute full browser sessions to navigate date pickers, station selection, and dynamic pricing tables accurately.
For extensive route networks, 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. We alert on null-rate spikes, layout changes, and coverage drops, responding before you notice any missing data.
Online travel agencies sync high-speed and regional schedules to offer comprehensive multi-modal journey planning.
Competitor transport operators track Renfe fare fluctuations to optimise their own pricing strategies.
Urban planners and consultants analyse passenger flow and train frequency to inform infrastructure investments.
B2B travel management tools integrate rail options alongside flights to enforce corporate booking policies.
Sustainability platforms compare train travel emissions against short-haul flights using accurate route distances.
Regional authorities monitor connectivity and capacity to forecast visitor influx during peak holiday seasons.
"Renfe dictates mobility across Spain, but extracting their schedules and dynamic pricing requires navigating strict session controls and heavily rendered search flows."
Most teams underestimate the investment required: reliable Renfe scraping requires Spanish residential proxies, full JavaScript rendering for their search forms, token management, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our renfe.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.
Playwright handles complex JavaScript rendering, search form interactions, and token lifecycles required by Renfe's booking engine.
We route requests through Spanish residential proxies to bypass regional blocks and rate limits imposed on data center IPs.
Pipelines run on AWS ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About renfe.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available timetables and pricing from Renfe is generally permissible for non-disruptive use cases. DataFlirt targets only public, non-authenticated schedule and fare data. We do not extract personal data or bypass authentication walls. Clients should review Renfe's terms of service and consult legal counsel for specific applications.
Renfe search sessions expire quickly. Our Playwright scripts manage token lifecycles automatically, refreshing sessions or initiating new search flows before timeouts occur to ensure continuous extraction.
Yes. We can extract schedules for both high-speed long-distance routes (AVE, Alvia, Avlo) and local commuter networks (Cercanías) across all major Spanish hubs.
We can configure pipelines to poll specific high-priority routes at hourly intervals. Full network sweeps typically run daily to capture advance purchase pricing curves.
Every pipeline run produces timestamped snapshots. We maintain a time-series table per route for fare class pricing and availability from the date your pipeline starts.
Our packages start at a defined route list (typically 100-500 origin-destination pairs) with daily delivery. For full network coverage, we price based on volume and frequency.
Absolutely. We provide a sample run of up to 20 routes as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily timetable dump or continuous fare monitoring across the Spanish rail network — we scope, build, and operate the pipeline. Tell us what you need.
