Case Study: Priceline reduces data storage costs and boosts recommendations with Starburst Data

Priceline Bolsters its Recommendation Engine and Reduces Data Storage Costs by 5X-10X with Starburst

Priceline, the global travel brand and part of Booking.com, needed a better way to democratize access to raw and historical data spread across Oracle RDBMS, Google Cloud Storage, BigQuery, and CloudSQL. The company wanted a secure, fast, and cost-effective solution that would support its move to a decentralized Data Mesh while helping users get insights without waiting on developers or data engineers. To address this, Priceline selected Starburst Data’s Starburst Enterprise.

With Starburst Data, Priceline deployed a federated query layer and semantic layer that lets users query and join data across heterogeneous sources while maintaining fine-grained security controls. The result is faster time-to-insight, near real-time analytics instead of day-or-two ETL delays, and a major reduction in storage costs—by 5X to 10X—by moving more data into unstructured formats without losing access. Starburst Data is also helping Priceline enhance recommendations, especially by tapping into more historical data for personalized shopper experiences.

Priceline

Sachin Gopalakrishna Menon

Senior Director of Data