TRAVEL DATA SCRAPING

Fares, rates and rooms,
across every OTA.

// THE SHORT ANSWER

Travel data scraping extracts fares, rates and availability from online travel agencies (OTAs) and metasearch — flight prices by route and date, hotel rates by property and room type, availability and rate-parity signals — delivered QA-verified as CSV, JSON or API. iWeb Data Scraping helps OTAs, airlines, hotels and travel-tech teams monitor competitor pricing and parity across a highly dynamic, perishable-inventory market.

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Key facts

  • Data point: Flight fares by route
  • Data point: Hotel rates by property
  • Data point: Room types & rates
  • Data point: Availability / inventory

Travel pricing is the most dynamic in retail — fares and rates change by the minute, vary by route, date, length of stay and demand, and inventory is perishable. Rate-parity teams manually check a fraction of routes and lose bookings on the rest. Travel data scraping systematizes it across every OTA and metasearch source.

We capture fares, hotel rates, room types and availability normalized by route, date band and room category, powering competitive pricing and rate-parity monitoring. It runs on our managed pipeline with real-time options for the most volatile fares.

THE POINT

One vertical, every platform that matters in it — matched into a single feed your team actually uses.

DATA POINTS WE EXTRACT

Perishable inventory, tracked live.

Flight fares by route
Hotel rates by property
Room types & rates
Availability / inventory
Rate-parity signals
Fare class / cabin
Length-of-stay pricing
Taxes & fees
Cancellation terms
Package deals
Loyalty pricing
Demand-based changes
SEE THE DATA FIRST

What you'll actually receive.

Real sample structure from this feed. Your free 48-hour sample comes in your category, in this shape — CSV, JSON or straight to your warehouse.

Travel feed — fares/rates by route, date, provider.
type provider route_or_hotel date price availability captured_at
flight AirExample DEL-BOM 2026-08-01 4299 available 2026-07-08
flight RivalAir DEL-BOM 2026-08-01 3999 available 2026-07-08
hotel StayInn Mumbai Central 2026-08-01 5400 3 left 2026-07-08
hotel GrandStay Mumbai Central 2026-08-01 6200 available 2026-07-08
↑ Sample structure — illustrative values. Your data reflects your platforms and category. Get this for your data →
WHO USES THIS

Built for the person
who owns the number.

OTA / METASEARCH

Rate parity at scale

Competitor fares and hotel rates across 12+ OTAs, normalized by route and room.

AIRLINE / HOTEL

Competitive pricing

Rival pricing by route/property and date, feeding revenue management.

TRAVEL-TECH

Power your product

Structured fare and rate feeds behind your comparison or booking product.

RELATED:Managed Web Scraping →Real-Time Scraping →Price Intelligence →
FAQ

Before the first call.

Flight fares by route and date, hotel rates by property and room type, availability and inventory, rate-parity signals, fare class/cabin, length-of-stay pricing, taxes and fees, cancellation terms, package deals, loyalty pricing and demand-based changes — across OTAs and metasearch sites, normalized for comparison.

Travel is the most volatile pricing category, so capture frequency is tuned to it: high-frequency for fares that move by the minute, scheduled for more stable hotel rates, with real-time extraction available where minutes of latency affect parity decisions. Each capture is timestamped.

Yes — rate-parity monitoring is a core use case. We capture the same route or property across many OTAs and metasearch sites, normalized by date band and room type, so parity violations and competitive gaps surface across your full inventory rather than the fraction manual checks cover.

Major OTAs (MakeMyTrip, Booking.com, Expedia, Agoda and regional players), metasearch (Google Flights, Skyscanner, Kayak), and airline and hotel direct sites. The mix is scoped to your competitive set and markets.

Get a free sample dataset in 48 hours.