Inside Tripadvisor’s Real-Time Personalization with ScyllaDB + AWS

Tripadvisor leverages ScyllaDB and AWS to deliver real-time personalized recommendations to its vast user base, enhancing user experience by quickly adapting to individual preferences. Using machine learning models, Tripadvisor processes over two billion requests daily, serving 400 million unique monthly visitors with tailored suggestions for hotels, attractions, and experiences. The company's personalization efforts are supported by a robust architecture that includes independently scalable microservices, a custom feature store, and a dual-database system that separates real-time data processing from offline data warehousing to ensure performance efficiency. ScyllaDB, chosen for its low-latency capabilities, replaced Cassandra due to its enhanced throughput and reduced operational demands, facilitating rapid data retrieval and processing. This infrastructure supports Tripadvisor's goal of offering seamless, relevant content to travelers, helping them plan ideal trips by utilizing advanced data engineering and machine learning practices.

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