Internship: Distributed pre-construction of airfare data content

Description de l'offre

Join us and shape the future of travel

Shaping the future of travel has always been important to us at Amadeus. Today, with technology getting smarter by the minute, that future is more exciting than ever.

We work at the heart of the global travel industry. Amadeus offers you the opportunity to learn and grow and drive your own progression in an exciting and multicultural environment.

Our people are driven by a passion for 'Where next?' If you want to shape your career and the future of travel, Amadeus is the place for you.

Team Description

Our team is responsible for designing, implementing and maintaining services to distribute airfares and fare-related data toAmadeus flight search engines:

· Load and create fares (specified, constructed or dynamic).
· Handle core processing of rules restrictions validation.
· Hide the complexity of sophisticated industry-driven fares and rules data models.
· Contribute to continuously optimizing price search and computation.

Our greatest challenge is to support increasing trends in travel industry: always more airfares and complex restrictions defined by travel providers, larger search criteria and more diversified travel options offered to the end-users.

Main Responsibilities

The trainee will participate to the evaluation of a new approach of fares and rules data processing based on distributed computing (Hadoop, MapReduce, Apache Spark).

Institutional data providers define a match/expand/filter data model that allows, from simple sequences of records, generating complex networks of tariffs and restrictions and, hence, determining the exact price and conditions matching any kind of travel. The trainee will contribute to prototyping and benchmarking the pre-computation of this data content. As part of his internship, s/he will:

· Understand industry data models characterizing fares and rules data.
· Apprehend the current optimizations that allow efficient context-full processing on-line in commercial transactions.
· Study algorithms that would allow scalable context-less construction off-line in database applications.
· Join to the current initiatives to transform our data infrastructure (data mining, cloud storage)

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