We extract flight schedules, dynamic pricing, route availability, and booking fare classes from Tarom.ro. 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 tarom.ro. All fields typed and schema-versioned.
"flight_number": "RO381", "origin_iata": "OTP", "destination_iata": "CDG", "departure_time_local": "2024-11-15T08:40:00", "arrival_time_local": "2024-11-15T10:50:00", "aircraft_type": "Boeing 737-800", "duration_minutes": 190, "stops": 0
| # | flight_number | origin_iata | destination_iata | departure_time_local | arrival_time_local | aircraft_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from tarom.ro. All fields typed and schema-versioned.
"flight_number": "RO381", "departure_date": "2024-11-15", "fare_class_name": "Eco Flex", "base_price": 145.0, "taxes": 42.5, "total_price": 187.5, "currency": "EUR", "seats_remaining": 4
| # | flight_number | departure_date | fare_class_name | base_price | taxes | total_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from tarom.ro. All fields typed and schema-versioned.
"origin_code": "OTP", "destination_code": "AMS", "distance_km": 1780, "frequency_per_week": 14, "operating_carrier": "Tarom", "codeshare_partners": "['KLM', 'Air France']", "seasonal_route": false, "active_status": true
| # | origin_code | destination_code | distance_km | frequency_per_week | operating_carrier | codeshare_partners |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fleet & Aircraft objects from tarom.ro. All fields typed and schema-versioned.
"aircraft_code": "738", "manufacturer": "Boeing", "model": "737-800", "passenger_capacity": 160, "cruise_speed_kmh": 842, "max_range_km": 5436, "seat_pitch_inches": 30, "wifi_available": false
| # | aircraft_code | manufacturer | model | passenger_capacity | cruise_speed_kmh | max_range_km |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Baggage & Ancillaries objects from tarom.ro. All fields typed and schema-versioned.
"fare_type": "Eco Light", "cabin_bag_allowance_kg": 8, "checked_bag_allowance_kg": 0, "extra_bag_fee": 45.0, "seat_selection_fee": 15.0, "meal_included": false, "refund_penalty": 100.0
| # | fare_type | cabin_bag_allowance_kg | checked_bag_allowance_kg | extra_bag_fee | seat_selection_fee | priority_boarding_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Tarom.ro scraper bypasses booking engine bot protections to extract real-time flight availability, dynamic pricing, and schedule changes across the entire route network.
Capture base fares, taxes, and total prices across all cabin classes and fare families.
Track departure times, arrival times, and planned aircraft types for all active routes.
Extract remaining seat counts for specific fare buckets to gauge flight load factors.
Distinguish between Tarom-operated flights and partner airline code-shares (e.g., KLM, Air France).
Extract pricing in RON, EUR, USD, and other supported display currencies.
Map baggage allowances and ancillary fees to specific fare families and routes.
Catalogue all active origin-destination pairs and track weekly flight frequencies.
Detect sudden price drops or surges on monitored routes using hash-based change detection.
Handle session timeouts and booking engine rate limits automatically without dropping data.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and frequency requirements. We design the extraction schema.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for tarom.ro.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Airline sites use complex session management and dynamic tokens. Here is how we ensure reliable data extraction.
Airline booking flows require sequential token passing. We maintain stateful Playwright sessions to navigate the search-to-results funnel without triggering invalid session errors.
Booking engines block data-center IPs. We route requests through EU residential proxies with realistic browser fingerprints to maintain access.
Flight results load asynchronously. We execute full JavaScript rendering to capture delayed pricing widgets and availability indicators.
We modulate request volume based on server response times, preventing IP bans while ensuring data freshness for critical routes.
We use fallback chains (CSS, XPath, regex) to extract flight data, ensuring pipeline stability even when Tarom updates their frontend.
OTAs and competing airlines track Tarom's dynamic pricing to adjust their own fare structures.
Aviation analysts monitor flight frequencies and seat availability to estimate route load factors.
Meta-search engines integrate direct pricing data to supplement delayed GDS feeds.
Enterprise procurement teams monitor historical fare data to negotiate corporate rates.
Insurers and logistics firms track schedule changes and cancellations for risk modeling.
Travel agencies trigger automated repricing workflows based on real-time seat availability drops.
"Airline pricing is notoriously volatile. Accessing raw, queryable flight data directly from the carrier provides a significant edge over delayed GDS feeds."
Extracting data from airline booking engines requires navigating strict session management, anti-bot protections, and heavy JavaScript rendering. DataFlirt handles these infrastructure challenges, delivering structured flight and pricing data directly to your warehouse so your team can focus on yield management and market analysis.
Everything supported by our tarom.ro 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 route iteration while Playwright manages the stateful booking flow required to surface accurate pricing.
We utilize localized European residential IP pools to ensure consistent access to regional pricing and avoid geo-blocks.
Pipelines execute on Kubernetes clusters, pushing structured Parquet or JSON directly to your S3 or BigQuery environment.
Data delivered to where your team already works — no new tooling required.
About tarom.ro scraping, legality, and pipeline operations.
Ask us directly →Yes. We configure pipelines to query specific origin-destination pairs across a rolling window of departure dates, capturing the full pricing matrix.
Our Playwright integration maintains stateful browser sessions, correctly passing the required tokens between the search form and the results page to prevent session errors.
Yes. We extract pricing and availability for Eco Light, Eco Flex, Business, and other available fare families on the results page.
Yes. We extract all flights returned in the search results, explicitly flagging code-share flights and identifying the operating carrier (e.g., Air France, KLM).
For targeted route lists, we can configure hourly or sub-hourly pipelines. For broad network sweeps, daily or twice-daily cadences are typical to balance data freshness with compute costs.
Yes. We map the included baggage allowances and ancillary fee structures associated with each specific fare class.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need daily schedule updates or high-frequency price monitoring for key routes — we construct and manage the infrastructure. Define your requirements.
