We extract flight itineraries, hotel rates, car rentals, and OTA price comparisons from Liligo. 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 liligo.com. All fields typed and schema-versioned.
"route_id": "CDG-JFK-20261015", "departure_airport": "CDG", "arrival_airport": "JFK", "airline": "Air France", "price": 482.5, "currency": "EUR", "duration": "8h 15m", "stops": 0, "ota_provider": "GoToGate"
| # | route_id | departure_airport | arrival_airport | departure_time | arrival_time | airline |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Hotels objects from liligo.com. All fields typed and schema-versioned.
"hotel_id": "HTL-99281", "name": "Novotel Paris Centre Tour Eiffel", "star_rating": 4, "price_per_night": 185.0, "total_price": 555.0, "currency": "EUR", "provider": "Booking.com", "review_score": 8.2
| # | hotel_id | name | location | star_rating | check_in | check_out |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Buses & Trains objects from liligo.com. All fields typed and schema-versioned.
"origin_station": "Paris Bercy", "destination_station": "Lyon Part Dieu", "operator": "FlixBus", "departure_time": "2026-10-15T08:30:00Z", "duration": "5h 45m", "price": 24.99, "currency": "EUR", "transfer_count": 0
| # | route_id | origin_station | destination_station | departure_time | arrival_time | operator |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Car Rentals objects from liligo.com. All fields typed and schema-versioned.
"pickup_location": "CDG Airport", "car_type": "Compact", "transmission": "Automatic", "agency": "Hertz", "price": 45.5, "currency": "EUR", "mileage_policy": "Unlimited", "insurance_included": false
| # | pickup_location | dropoff_location | pickup_time | dropoff_time | car_type | transmission |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for OTA Comparisons objects from liligo.com. All fields typed and schema-versioned.
"service_type": "flight", "provider_name": "Expedia", "base_price": 410.0, "taxes": 72.5, "total_price": 482.5, "currency": "EUR", "is_sponsored": false, "timestamp": "2026-05-12T14:22:10Z"
| # | search_id | service_type | provider_name | provider_url | base_price | taxes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Liligo scraper handles complex multi-city searches, date permutations, and dynamic OTA pricing. We manage the session states and JavaScript rendering required to extract accurate travel data.
Capture airlines, flight numbers, layovers, durations, and baggage policies across all available routes.
Extract room types, nightly rates, total prices, and OTA provider comparisons for specific dates and locations.
Scrape bus and train schedules, operators, transfer points, and ticket prices across European and global routes.
Track rental agency prices, vehicle categories, transmission types, and mileage policies per location.
Compare prices across multiple online travel agencies for the exact same flight or hotel listing.
Automate searches across flexible date matrices to map out price curves and seasonal trends.
Extract native currency values and normalise them to your base currency for accurate analysis.
Capture volatile travel pricing with intraday pipelines to spot flash sales and algorithmic price changes.
Maintain active browser sessions to prevent search timeouts and ensure complete result loading.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, or hotel locations. We design the extraction schema together.
We configure Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for liligo.com.
Schema validation, null-rate checks, price-outlier detection, and sample itineraries before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Travel aggregators employ aggressive anti-bot measures and complex session states. Here is how we maintain stable extraction.
Travel sites use sophisticated bot protection. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to mimic legitimate human search behaviour.
Liligo's search results load dynamically via complex JavaScript applications. We run full Playwright browser sessions to trigger lazy-loading, wait for all OTA partners to return prices, and capture the final rendered state.
Flight searches expire quickly. Our pipeline manages search initiation tokens, handles polling endpoints, and extracts data before session expiration, ensuring complete datasets for every origin-destination pair.
We normalise diverse data points like layover durations, baggage inclusions, and multi-leg carrier combinations into a clean, predictable schema ready for immediate database insertion.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing OTA partners, and schema drift, responding before you notice any data gaps.
Airlines and hotel chains monitor OTA pricing to ensure compliance with rate parity agreements across distribution channels.
Travel operators ingest competitor pricing to feed their own dynamic pricing algorithms and maximise yield.
Aviation analysts track flight frequencies, carrier competition, and pricing trends to evaluate new route viability.
Hedge funds and market researchers use aggregated search pricing as a leading indicator for seasonal travel demand.
OTAs track how their prices rank against competitors on meta-search engines like Liligo to optimise their bidding strategies.
Machine learning teams use historical travel pricing datasets to train predictive models for fare forecasting.
"Travel pricing is the ultimate dynamic dataset. If you cannot capture the exact price at the exact moment across all providers, your analysis is already obsolete."
Most teams underestimate the complexity of scraping travel aggregators. Reliable extraction requires residential proxies, full JavaScript execution, session token management, and complex date permutation logic. DataFlirt absorbs this infrastructure burden so your analysts can focus on yield management, not bot mitigation.
Everything supported by our liligo.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 and date permutations. Playwright handles JavaScript rendering, session tokens, and waiting for OTA price resolution.
We maintain pools of residential ISP proxies to bypass strict travel aggregator bot protection. Rotation happens per search session to maintain state.
Pipelines run on AWS Lambda and ECS. Airflow handles complex date-matrix scheduling and dependency management. All state stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About liligo.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated flight, hotel, and transport data. We do not extract personal data or bypass authentication walls. Clients should review terms of service and consult legal counsel for specific commercial use cases.
Travel meta-search engines often timeout if all OTA partners do not respond quickly. Our Playwright scripts manage session keep-alives, handle polling endpoints, and wait for network idle states to ensure we capture the complete set of provider prices before session expiration.
Yes. We can configure pipelines to search origin-destination pairs across a rolling 30, 60, or 90-day window, capturing price curves and identifying the cheapest travel days.
Flight and hotel pricing is highly volatile. We can run pipelines at intraday frequencies (e.g., every 4 hours) for critical routes, ensuring you capture algorithmic price adjustments and flash sales.
Yes. Our standard flight schema includes layover airports, layover durations, total travel time, operating carriers, and basic baggage inclusion flags.
Our smallest packages start at a defined list of routes (e.g., 500 origin-destination pairs) with daily delivery. For larger matrices covering thousands of routes, we price based on compute volume and delivery frequency.
Absolutely. We provide a sample run of up to 50 routes or hotel locations as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need daily hotel rate tracking or intraday flight price monitoring across thousands of routes, we build and operate the infrastructure. Tell us what you need.
