Powering the search for a 360 view of travelers
Travelport manages more than 200 million bookings a year — over 117 million airline tickets, 65 million hotel nights, and 94 million car rental days. And they store all that booking data on old school, but extremely powerful mainframes, a technology most commonly found in banks and government buildings. Aside from their processing power, these mainframes are so reliable that Abdoul Sylla, Travelport’s Sr. Director of Data Products, said that in the 12 years since he started working for Travelport, the mainframes have not gone down even once. The trade-off for this reliability and power is that these mainframes are designed to handle simple transactions, like ticket sales, not process intensive tasks like business analytics.
As their clients’ success increasingly depended upon delivering better target offers and bookings based on past customer behavior, so did Travelport’s need for a better analytics solution. Their mainframe solution worked perfectly for their transactional operations, but searching the mainframes by more than a few fields wasn’t an option. Nor was asking anything more than primary level questions (‘show me all the flights leaving Chicago today’ or ‘which hotels have rooms available tomorrow?’). Travelport needed to answer their travel agency clients’ more sophisticated queries (‘show me all the passengers arriving in LAX today who have traveled more than eight hours and who have no hotel booking’). And they needed to give those answers fast.
Sylla and his team of fifteen architects and developers saw the direction their industry was headed and anticipated that Travelport would soon require a new search infrastructure. Not a replacement for the existing mainframe, but a system that would run within the same ecosystem. About five years ago, they began their hunt for a faster, more flexible way to search the 200 million travel itineraries they get every year.