Travel & HospitalityBusiness Intelligence

How Booking.com Uses Snowflake Cortex AI to Unify 31M Travel Listings

Booking.com, one of the world’s largest online travel platforms with hundreds of millions of users and hundreds of petabytes of data, migrated from a fragile Hadoop ecosystem to Snowflake to modernize its data and AI infrastructure. The platform deployed Snowflake Cortex AI to give teams across the organization self-service access to unified data spanning 31 million listings across 175,000 destinations. The migration resolved critical peak-season scalability failures and positioned Booking.com to pursue its connected trip vision while meeting GDPR and EU AI Act compliance requirements.

Outcomes

31MTravel listings unified on platform
175,000Destinations powered by Cortex AI

Tools & Technologies

1S
Snowflake
Cloud data warehouse by Snowflake for storing, querying, and sharing structured and semi-structured data.
2SC
Snowflake Cortex AI
Built-in AI and ML capabilities within the Snowflake Data Cloud

AI Categories

Challenge

A fragile on-premises Hadoop ecosystem caused critical workload delays during peak travel seasons and made it impossible for teams to reliably discover and trust data at the scale required to power a connected trip experience across 220 countries.

Solution

Booking.com migrated to Snowflake’s cloud data platform and deployed Snowflake Cortex AI, centralizing Big Data and AI workloads in a unified, cataloged environment with native governance controls for GDPR and EU AI Act compliance.

Full Story

Booking.com operates at a scale that makes data infrastructure a strategic necessity rather than an operational detail. The platform connects hundreds of millions of users to 31 million travel listings — hotels, flights, rental cars, attractions — across 175,000 destinations in 220 countries and territories. At that volume, generating hundreds of petabytes of data, the ability to move quickly and reliably on insights determines whether the platform stays competitive.

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Source

SNOWFLAKE
June 2026
Original case study

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