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All use cases

TRAVEL INTELLIGENCE / MANAGED DATA

Skyscanner
flight data.

Compare journeys, not just the lowest number.

Managed Skyscanner flight-data collection for route research and fare comparison. Keep itineraries, booking providers and dated search context connected.

Discuss your project Explore data points

RESEARCH WITH THE RIGHT CONTEXT

One route. Many ways to get there.

Carrier, stops, cabin and booking provider can all change what a fare represents. Build a source-linked dataset around your route-date grid, with collection coverage agreed in advance.

01

Route and carrier research

Compare available journey options across selected airports and dates. Keep direct flights and connecting itineraries distinct.

02

Provider-level fare comparison

Compare offers for the same journey across booking providers, retaining available transfer and fare conditions.

03

Observed price movement

Repeat a consistent search scope to study fare changes. Build a dated history without treating a missing result as zero availability or a zero price.

DATA POINTS TO BUILD AROUND

The offer. The journey.
The context behind it.

Choose the Skyscanner fields your research needs. These are proposed normalized field names, not a live response or a guaranteed source schema. Availability is confirmed against your project scope.

01

Search definition

Route, dates, traveler mix, cabin and market define the comparison set.

Example field names
  • origin
  • destination
  • departure_date
  • return_date
  • travelers
  • cabin_class
  • market
  • currency
02

Journey structure

Preserve the itinerary, its outbound or return legs and the flight segments inside each leg.

Example field names
  • itinerary_reference
  • legs
  • segments
  • stops
  • duration_minutes
03

Flight details

Available airline, airport and schedule details, with local departure and arrival times explicitly labeled.

Example field names
  • carrier
  • origin_airport
  • destination_airport
  • departure_local
  • arrival_local
04

Provider offers

Keep booking-provider attribution, displayed price, currency and available booking links attached to the journey.

Example field names
  • booking_provider
  • price_amount
  • currency
  • price_basis
  • booking_url
05

Fare conditions

Transfer type, baggage and fare attributes where available. These are optional fields, not assumed inclusions.

Example field names
  • transfer_type
  • baggage_information
  • fare_attributes
  • conditions_available
06

Search provenance

Record collection time, price type, source and whether the observed search completed.

Example field names
  • captured_at
  • source_url
  • search_context
  • price_type
  • search_status
  • missing_fields

Start with the fields that change your decision. Add enrichment or cross-source matching as a separate, explicit part of the brief.

Plan your dataset

PRICE WITHOUT CONTEXT IS NOT ENOUGH

Keep comparable fares truly comparable.

A headline fare is only one part of an offer. Preserve the journey and booking conditions alongside it before ranking providers or measuring price changes.

Indicative versus dated results
Discovery prices and date-specific search quotes serve different purposes. Label them separately instead of combining them into one price series.
Journey versus booking option
A multi-segment itinerary can have several provider offers. Self-transfer and protected connections are not interchangeable products.
Observation versus guarantee
An observed fare can change before booking. Store capture time and completeness; do not present the dataset as a reservation or price guarantee.

MANAGED TRAVEL DATA

From a research brief
to a repeatable feed.

We collect the agreed source data, structure it for your workflow and define how collection quality and changes will be handled.

  1. 01 / SCOPE

    Define your search space

    Choose routes or destinations, dates, traveler profiles and markets. Confirm permitted access, required fields and a useful refresh schedule.

  2. 02 / VALIDATE

    Check a representative sample

    Review price basis, source attribution, missing fields and result completeness. Agree what counts as a comparable offer.

  3. 03 / DELIVER

    Connect your analysis

    Receive the agreed file format or scoped API integration. Define monitoring, failure reporting and maintenance before recurring delivery.

A maintained travel feed or a public-page analysis?

These datasets are a managed service. The self-service Scraping API collects accessible public content with optional AI analysis; it does not bypass CAPTCHA or login, or automatically return this travel schema.

Explore Scraping API

BEFORE YOU BUILD ON THE DATA

Details that matter
to your pipeline.

Why can the first search results be incomplete?

Skyscanner describes a progressive search workflow: early results can be a partial cached set while more providers respond. We scope completion checks and label incomplete observations rather than treating the first screen as complete coverage.

Are indicative prices the same as live search quotes?

No. Indicative prices support broad discovery from previously observed low prices. A date-specific live search is a different observation. Neither is a guaranteed booking price; pricing type and capture time should remain explicit.

Are baggage, fees and transfer protection always included?

No. Availability depends on the source and access scope. We preserve published conditions where available and flag unknowns. Protected connections, self-transfer and protected self-transfer are different booking types, not just different prices.

How are time zones and repeat-search matching handled?

Departure and arrival times can be airport-local. We retain their time context and use explicit duration values where available. Cross-run matching rules are agreed for routes, schedules, carriers and providers; session references are not assumed to be permanent IDs.

How are coverage, frequency and cost agreed?

We size the project around destinations or route-date combinations, required fields, traveler and market variants, and refresh frequency. After access and sample validation, we agree delivery and pricing. One search can produce many offers, so a returned-row count alone is not a reliable measure of collection cost.

Can the feed fit our existing data pipeline?

We can scope CSV, TSV or JSON files, or a project-specific API integration. We agree identifiers, null values, currency conventions, timestamps and matching rules before recurring delivery. Source changes, incomplete runs and maintenance responsibilities are included in the project discussion.

Official source references

Background for the data concepts on this page, not evidence of a partner API integration.

MORE TRAVEL DATA

Cross-source comparisons need explicit matching rules, not just similar prices.

YOUR SEARCH SCOPE. YOUR DATA FEED.

Build your
Skyscanner
dataset.

Tell us what you need to compare. We will review access, agree the search context and scope a sample before discussing scale.

  • Routes, destinations and travel dates
  • Required fields and price context
  • Refresh frequency and delivery format
Talk through your project

Up to 5,000 characters.

Projects are accepted individually, subject to feasibility and available capacity. An enquiry does not reserve a solution or confirm delivery. Pricing and terms are agreed directly with our team.

A real person reviews your brief and follows up about scope and delivery.

ScrapingBuddy is an independent data service, not affiliated with or endorsed by Skyscanner. We provide data collection, not travel booking or guaranteed fares. Hero artwork is illustrative. Access, coverage and delivery commitments are agreed per project.